Atomic layer deposition (ALD) relies on alternated, self-limiting reactions between gaseous reactants and an exposed solid surface to deposit highly conformal coatings with a thickness controlled at the submonolayer level. These advantages have rendered ALD a mainstream technique in microelectronics and have triggered growing interest in ALD for a variety of nanotechnology applications, including energy technologies. Often, the choice for ALD is related to the need for a conformal coating on a 3D nanostructured surface, making the conformality of ALD processes a key factor in actual applications. In this work, we aim to review the current status of knowledge about the conformality of ALD processes. We describe the basic concepts related to the conformality of ALD, including an overview of relevant gas transport regimes, definitions of exposure and sticking probability, and a distinction between different ALD growth types observed in high aspect ratio structures. In addition, aiming for a more standardized and direct comparison of reported results concerning the conformality of ALD processes, we propose a new concept, Equivalent Aspect Ratio (EAR), to describe 3D substrates and introduce standard ways to express thin film conformality. Other than the conventional aspect ratio, the EAR provides a measure for the ease of coatability by referring to a cylindrical hole as the reference structure. The different types of high aspect ratio structures and characterization approaches that have been used for quantifying the conformality of ALD processes are reviewed. The published experimental data on the conformality of thermal, plasma-enhanced, and ozone-based ALD processes are tabulated and discussed. Besides discussing the experimental results of conformality of ALD, we will also give an overview of the reported models for simulating the conformality of ALD. The different classes of models are discussed with special attention for the key assumptions typically used in the different modelling approaches. The influence of certain assumptions on simulated deposition thickness profiles is illustrated and discussed with the aim of shedding light on how deposition thickness profiles can provide insights into factors governing the surface chemistry of ALD processes. We hope that this review can serve as a starting point and reference work for new and expert researchers interested in the conformality of ALD and, at the same time, will trigger new research to further improve our understanding of this famous characteristic of ALD processes.

Atomic layer deposition (ALD) is a gas phase thin film deposition technique which has been discovered and developed independently in the 1960s in the Soviet Union and in 1974 in Finland.1–3 This technique is characterized by exposing the substrate to an alternating sequence of vapor phase reactants. Due to the self-saturating nature of the surface reactions, the film thickness can be controlled at the atomic scale.4–8 A typical ALD process consists of several ALD cycles with each ALD cycle comprising four characteristic steps, which are shown in Fig. 1 for the prototypic4–6 trimethylaluminum (TMA)/H2O process:

FIG. 1.

Schematic representation of one ALD cycle of the TMA/H2O process.

FIG. 1.

Schematic representation of one ALD cycle of the TMA/H2O process.

Close modal
  1. Step 1: The first reactant A (TMA) reacts in a self-terminating way with the available functional groups on the OH-terminated surface.

  2. Step 2: The excess of reactant A (TMA) and the gaseous by-products (CH4) are purged or pumped away.

  3. Step 3: The second reactant B (H2O) reacts in a self-terminating way with the adsorbed species (A) on the surface.

  4. Step 4: The excess of reactant B (H2O) and the gaseous by-products (CH4) are purged or pumped away.

In this review, we will call steps 1 and 3 the first and second reactions of the ALD cycle, respectively. After each ALD cycle, a certain amount of material (coating) is deposited on the surface, which is called the growth per cycle (GPC). The GPC can be expressed in various units such as those of the thickness (e.g., nm), mass gain (e.g., g), and an increase in the areal density (e.g., atoms/nm2).

As opposed to “line-of-sight” deposition techniques such as physical vapor deposition (PVD),9,10 ALD has the capability to grow uniform and conformal films in 3D structures with complex shapes and with a large depth to width ratio or in more general terms a large aspect ratio.11–15 For deposition techniques that are flux controlled [such as chemical vapor deposition (CVD)16 and PVD], film growth depends on the local gas flux. Because of the inherent kinetics of gas transport within narrow trenches, the flux of reactant molecules can be several orders of magnitude larger near the entrance as compared to the bottom of the structure. Therefore, the entrance region to narrow trenches tends to get clogged at the beginning of the deposition, making it difficult for reactant molecules to diffuse deeper into the structure. Various approaches exist to improve the conformality of CVD processes, e.g., inhibiting the growth,17 using pulsed CVD,18,19 and using surfactants to catalyze the growth.20,21 Also during ALD deposition, the entrance region will receive a higher flux of reactant molecules. However, the self-saturating nature of the surface reactions during ALD results in surface-controlled deposition. Higher flux near the entrance region will result in faster coverage at this location, but once the surface is saturated, no further reaction can take place at this site, avoiding clogging of the trench entrance and allowing the reactant molecules to diffuse deeper into the trench and coat the entire structure.

The miniaturization of semiconductor devices leads to the introduction of more complex 3D geometries with an increasing aspect ratio, often termed high aspect ratio (HAR) structures. Due to the self-limited surface reactions, ALD is one of the most suitable deposition techniques to deposit thin coatings with an excellent control of the layer thickness onto such structures. ALD is, for example, used for the fabrication of trench capacitors in dynamic random-access memory (DRAM),22,23 in FinFETs,24,25 and 3D NAND structures.26,27 Also, outside the field of semiconductor applications,28 ALD is being considered to deposit films onto 3D substrates, e.g., for surface functionalization and protection of microelectromechanical systems (MEMSs),29,30 and coatings for fuel cells,31 solar cells,32–34 batteries,35–39 catalytic surfaces,40–46 membranes,47,48 textiles,49 and pharmaceuticals.50 

In this review article, we explore the status of current understanding of the conformality of ALD processes, providing an overview of published experimental and theoretical studies focusing on this topic. First, conformality-related concepts of ALD processes will be introduced in Sec. II. In Sec. III, we discuss methods and dedicated test structures that have been used to quantify the conformality of ALD processes. An overview of experimental reports on the conformality of thermal, plasma-enhanced, and ozone-based ALD processes is presented in Sec. IV. In Sec. V, different models are discussed that have been used to simulate ALD in narrow structures. These models can be used to optimize process parameters towards improved conformality. Fitting of model parameters to experimental data can also provide insights into the ALD surface reactions, e.g., in sticking probabilities and the impact of non-ideal side-reactions during ALD. In Sec. VI, we simulate the influence of different ALD parameters on the conformality with a Monte Carlo model51 and discuss how one can obtain relevant information on the growth mechanism from the analysis of such thickness profiles.

Due to the self-limited nature of the chemisorption and subsequent surface reactions, it is possible to grow with ALD uniform and conformal films in structures with a large depth to width ratio. Uniform films have an equal thickness and composition (and other properties) at each position along a planar substrate, e.g., along a 300 mm wafer. Conformal films have the same thickness (and properties) also inside 3D features (“around-the-corner”). Figure 2 shows a schematic representation of the coating of a 3D structured surface with a non-conformal line-of-sight technique (a) and with the typical conformality of an ALD coating (b). In the absence of the 3D feature, both films could be considered uniform.

FIG. 2.

Schematic representation of a deposition on a 3D structured surface by a line-of-sight technique (a) and by a conformal technique such as ALD (b).

FIG. 2.

Schematic representation of a deposition on a 3D structured surface by a line-of-sight technique (a) and by a conformal technique such as ALD (b).

Close modal

During reactant transport, the reactant molecules penetrate into narrow structures in a certain flow regime. To distinguish the different flow regimes, Knudsen52 introduced a dimensionless parameter, the Knudsen number Kn, which is defined as the ratio of the mean free path λ (m) and the pore diameter dp (m)

Kn=λdp.
(1)

If the mean free path of the reactant molecules is much larger than the dimensions of the structures (Kn ≫ 1), gas transport takes place in the molecular flow regime.52 In this regime, the transport is dominated by particle-surface interactions and inter-particle interactions can in most cases be neglected.

The mean free path λ (m) of molecules is given by53 

λ=kBT2πd2P,
(2)

with kB (m2 kg/(s2 K)) being the Boltzmann constant, T (K) being the temperature, P (Pa) being the pressure, and d (m) being the diameter of the molecules. The mean free path depends strongly on pressure, while the molecule size and temperature affect the mean free path to a minor extent, as illustrated in Fig. 3. At sufficiently low pressure, a molecular flow regime can be obtained in structures with macroscopic (i.e., mm to cm) dimensions, while for near-atmosphere pressures, molecular flow will only be achieved in nanometer scale features.

FIG. 3.

Mean free path (left y-axis) as a function of pressure, calculated according to Eq. (2), for molecules with average diameters of 5, 7, and 9 Å at a temperature of 100 °C (a) and for a molecule with an average diameter of 7 Å at temperatures of 100 °C, 300 °C, and 500 °C (b). The working pressure regimes of the pump-type, flow-type, and atmospheric pressure (AP-type) ALD reactors are indicated in the figure. The right y-axis of the graphs shows the characteristic feature size (dp). Comparing dp with the mean free path, λ, allows to determine the corresponding flow regime for a given pressure: molecular flow regime (λdp) and viscous flow regime (λdp).

FIG. 3.

Mean free path (left y-axis) as a function of pressure, calculated according to Eq. (2), for molecules with average diameters of 5, 7, and 9 Å at a temperature of 100 °C (a) and for a molecule with an average diameter of 7 Å at temperatures of 100 °C, 300 °C, and 500 °C (b). The working pressure regimes of the pump-type, flow-type, and atmospheric pressure (AP-type) ALD reactors are indicated in the figure. The right y-axis of the graphs shows the characteristic feature size (dp). Comparing dp with the mean free path, λ, allows to determine the corresponding flow regime for a given pressure: molecular flow regime (λdp) and viscous flow regime (λdp).

Close modal

If the mean free path of the reactant molecules is much smaller than the dimensions of the features (Kn ≪ 1), gas transport is governed by viscous flow,52 also known as continuum flow.54 In viscous flow, there are frequent particle-particle interactions in the gas phase. The transition flow between molecular and viscous flow takes place when Kn ≈ 1 and is often termed Knudsen flow although the latter term is also used as a synonym for molecular flow.16 

When the gas phase consists of different types of molecules, e.g., when a mixture of reactant molecules (species A) and carrier gas molecules (species B) is used in ALD, one has to adapt the above formula [Eq. (2)] of the mean free path. Chapman et al.55 derived a specific scattering length λ0,A(m) for a particle of species A taking into account the interaction with particles of species B

λ0,A=kBT2PAσA,A+1+mAmBPBσA,B,
(3)

with kB (m2 kg/(s2 K)) being the Boltzmann constant, T (K) being the deposition temperature, Pi (Pa) being the partial pressure, and mi (kg) being the mass of molecules of type i ∈ {A, B}. The cross-section between the molecules i and j with radii ri (m) and rj (m) is represented by55 

σi,j=π(ri2+rj2).
(4)

One of the distinguishing factors among the different types of ALD reactors is whether they operate in the temporal or spatial ALD mode, with the first mode being the most conventional one. During temporal ALD, the sample is stationary and the different reactants are sequentially injected and removed from the sample cell. In spatial ALD, there is a continuous supply of the reactants in isolated injection regions which are separated by an inert gas curtain, while the substrate moves between the different zones.56–58 Suntola developed in 1974 his first ALD reactor where he applied spatial ALD.59 The reactor comprised a carousel that rotated at several rounds per second and worked at a base pressure of 10−4 Pa.3 In another reactor design, Suntola et al.60 used a carrier gas to separate the different surface reaction steps from each other in a temporal deposition mode.

As discussed above, the reactant (partial) pressure is a determining factor for the gas transport regime in high aspect ratio structures, influencing in turn the process of conformal coating during ALD. Therefore, we classify the different designs of ALD reactors according to the typical pressure ranges that are applied. Following the classification by George,4 we define pump-type and flow-type (temporal) ALD reactors. In pump-type reactors, reactants are added into the reactor chamber without the use of a carrier gas and the typical reactant pressures are in the range of 10−3–100 Pa. After the reactant exposure, the chamber is evacuated by pumping down to a base pressure in the range of 10−4–10−3 Pa. Because of the low pressure and the corresponding molecular flow regime, the reactor design is not restricted to specific geometric constraints and can easily be adapted to accommodate plasma sources and in-situ characterization techniques. The main disadvantage of pump-type reactors concerns the long cycle times in the range of 101–102 s, due to the slow evacuation of the reaction chamber without the use of a purge gas. In the classical flow-type reactor design,3,60 the reactants are entrained in an inert carrier gas which flows through the reactor in a viscous flow regime. Flow-type reactors are typically operated at a pressure near 100 Pa, and cycle times are on the order of 100 s.4 Next to pump-type and flow-type reactors, atmospheric pressure (AP-type) reactors in which ALD processes take place at (or near) atmospheric pressure (∼105 Pa) form the third class of ALD reactors. Over the past few years, there has been increasing interest in spatial ALD approaches applying atmospheric pressures for high throughput ALD for a number of applications including photovoltaics.56–58 In this way, high deposition rates can be achieved for certain processes, e.g., ∼1 nm/s for ZnO.61 

As shown in Fig. 3, different flow regimes occur during ALD within high aspect ratio structures, depending on the characteristic size of the 3D features that are present on the substrate and the absolute pressure range, linked to the type of ALD reactor used. For instance, in millimeter-sized features, reactant transport occurs in the molecular flow regime in pump-type reactors, but in flow-type and AP-type ALD reactors, it occurs in the viscous flow regime.

Pressure plays a crucial role in the ALD process, as it determines the impinging flux of reactant molecules on the substrate. At a given pressure, a certain minimum amount of time is needed before the sample surface is fully covered with adsorbed reactant molecules and saturation is reached. A useful measure for the reactant exposure is therefore given by the product of the reactant partial pressure and the pulse time. In this way, the exposure is expressed in Langmuir (1 L = 106 Torr s = 7500 Pa s). As an example, Gordon et al.62 estimated that the exposure required to saturate a flat surface with Hf(NMe2)4 molecules during ALD of HfO2 at 200 °C is in the range of 3–43 L. Much larger exposures are commonly required during ALD on high aspect ratio structures to compensate for diffusional limitations. To deposit a conformal coating of HfO2 into holes with a pore diameter of 0.17 μm and a depth of 7.3 μm, an exposure of 9000 L was required.62 Applying sufficiently large exposures is one of the essential conditions to obtain a conformal coating by ALD as will be exemplified later in this review article.

The surface reaction kinetics of ALD processes can be complex5,6,63 and should not be considered as trivial as depicted in Fig. 1. For many processes, even the reaction stoichiometry during an ALD cycle is not necessarily known.64 To cope with this complexity when modelling the conformality of ALD processes, the reaction chemistry is often simplified by using irreversible Langmuir surface kinetics.65–67 The sticking probability s is introduced as the probability that a reactant molecule reacts upon collision with the surface and contributes to film growth. It is often assumed that the sticking probability has a first order dependence with the available surface sites16 

s=s0(1θ),
(5)

with s0 being the initial sticking coefficient (i.e., reaction probability with a bare surface) and θ being the fraction of covered sites. This expression implies that the surface reactivity gradually decreases with the increase in the coverage and eventually becomes zero. This simple model thus reflects the self-limiting nature of the surface reactions during ALD, while the reaction kinetics (fast/slow) can be implemented via the initial sticking coefficient s0 (high/low values).

Taking a step back and considering in the first instance reversible Langmuir adsorption, the first reaction of an ALD cycle (Reaction A) can be represented by

Ag+*A*,
(6)

with Ag being the gaseous reactant A, * a vacant surface site, and A* the chemisorbed reactant A. A* can be interpreted as a “lumped reaction product” (and not the result of an elementary reaction), containing multiple types of surface species simultaneously.

The adsorption rate rads is equal to the product of the adsorption rate constant kads, the partial pressure of reactant A, PA, and the fraction of uncovered surface sites, giving the overall second-order68 surface reaction rate equation

rads=kadsPA(1θ).
(7)

The desorption rate rdes is equal to the fraction of covered sites times the desorption rate constant kdes

rdes=kdesθ.
(8)

The rate of change in the surface coverage θ is obtained by subtracting the desorption rate from the adsorption rate

dθdt=radsrdes.
(9)

At equilibrium, dθdt is zero. In practice, gaseous byproducts are often continuously pumped out, making reverse reactions unlikely and justifying the assumption of irreversibility. When irreversible adsorption is assumed, the second term, rdes, in Eq. (9) will be ignored.

The second reaction of the ALD cycle (reaction B) can be described, in the lumped way, assuming an “average” reaction product AB*

A*+BgAB*.
(10)

In practice, in many models, reaction B is not modelled separately, and it is only considered that the surface left behind by exposure to reactant A, saturated with θ ≈ 1, is rendered reactive again in reaction B.

Furthermore, it is often assumed that the total number of adsorption sites for the Langmuir adsorption model is obtained from the number of metal atoms deposited per cycle and thus growth per cycle (GPC). The current authors understand this to be a gross oversimplification, as, e.g., the number of OH groups on alumina, which act as (reactive) adsorption sites in the trimethylaluminum reaction, is known to be 7–9 per nm2 at typical ALD Al2O3 conditions, while the number of aluminium atoms deposited per cycle is around 4.5 per nm2.5 Despite the fact that this assumption is clearly oversimplified, it is being repeatedly used as it can give modelling results with a satisfactory fit.

For radical-assisted ALD processes (e.g., plasma-enhanced or PE-ALD) or ozone-based ALD processes, reactant molecules that collide with the surface can undergo recombination processes. For example, an O radical can recombine with an adsorbed O atom and form molecular O2 that leaves the surface. The probability that a species recombines during a collision with the surface is usually defined as the recombination probability r.66,69,70 Although it depends on the process, the recombined species can often no longer contribute to film growth upon subsequent collisions with the surface, e.g., when the surface is only reactive towards O radicals and not towards molecular O2. Hence, recombination processes are usually considered loss processes.

Elam et al.65 introduced the concepts of a diffusion versus a reaction limited growth type for ALD growth in narrow features in the molecular flow regime.71 In the diffusion limited growth type, it is the geometry of the feature that causes the main difficulty in coating.72 During deposition, the most accessible sites will be covered first, resulting in a clear front between the accessible (covered) sites and the less accessible (uncovered) sites. During the deposition, this front will gradually penetrate deeper into the feature. In the reaction limited growth type, the difficulty of coating is mainly related to a low sticking probability of the reactant molecules. In this case, there is a less sharp front between coated and as yet uncoated parts of the feature. For plasma-enhanced ALD, Knoops et al.66 introduced a recombination limited growth type, where saturation is not limited by the diffusion rate or the sticking probability but by the recombination loss during collisions of the radicals with the side-walls of the feature. These different growth types are illustrated in Fig. 4, which shows unsaturated thickness profiles. Note that both the aspect ratio of the feature and the sticking probability of the ALD reactants will determine the ALD growth type, as will be discussed in Sec. VI B 4 and in Fig. 23.

FIG. 4.

Schematic representation of unsaturated thickness profiles: diffusion limited (a), reaction limited (b), and recombination limited growth type (c). Adapted with permission from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010). Copyright 2010 The Electrochemical Society.

FIG. 4.

Schematic representation of unsaturated thickness profiles: diffusion limited (a), reaction limited (b), and recombination limited growth type (c). Adapted with permission from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010). Copyright 2010 The Electrochemical Society.

Close modal

The Aspect Ratio (AR) of a structure is typically defined as

AR=Lw,
(11)

with L (m) being the depth and w (m) being the width of the structure, as indicated in Fig. 5. Achieving a conformal coating in a structure becomes more difficult with an increase in the AR of the structure. In addition, it will be easier to coat an elongated trench with depth L and width w than a cylindrical hole with the same depth L and diameter w because the opening through which the reactant can enter will be larger for the trench which facilitates the diffusion. AR is a 2D concept, based on geometrical measures of a cross-section of a 3D object, and is therefore not expected to be sufficient as a parameter to fully describe the difficulty of precursor diffusion into 3D features. Therefore, not only the AR will be important but also the length of the feature along the third dimension will have an impact on the conformality. A schematic representation of a hole and trench structure with equal AR is given in Fig. 5.

FIG. 5.

Schematic representation of a cylindrical hole (a), a square hole (b), and a trench structure (c) with width w and depth L. The three structures have the same AR; however, the holes [(a) and (b)] have a different EAR than the trench structure (c).

FIG. 5.

Schematic representation of a cylindrical hole (a), a square hole (b), and a trench structure (c) with width w and depth L. The three structures have the same AR; however, the holes [(a) and (b)] have a different EAR than the trench structure (c).

Close modal

Gordon et al.62 proposed a generalized expression for the aspect ratio a of holes

a=Lp4A,
(12)

with L (m) being the depth of the hole, p (m) being its perimeter, and A (m2) being the cross-sectional area. This expression takes into account the 3D nature (third dimension) of the feature. For cylindrical holes, Eq. (12) reduces to L/w which is equal to the depth to width AR interpretation from Eq. (11). For trenches, Eq. (12) reduces to L/(2w), which is a factor of two smaller than the conventionally used AR of Eq. (11). Gordon et al. also showed that for large a, the required exposure for conformal coating scales with a2. The difference of a with a factor of two between trenches and cylinders implies that a four times larger exposure is required to conformally coat a hole in comparison with a trench with the same depth to width ratio (AR) as can be measured in a cross-sectional view. This example of trenches versus holes clearly illustrates that a depth to width ratio of a 3D feature is not a sufficient measure to estimate the difficulty in coating the 3D structure: gas flow into the real structure will depend on the full 3D geometry of the feature.

To facilitate a direct comparison between different structures and literature reports, we propose a new concept to express the aspect ratio in a structure-independent way. Analogous to the “equivalent oxide thickness” which was introduced in the field of high-k oxides to enable a straightforward comparison between different structures and materials by expressing their key functional properties with respect to a well-known reference material (SiO2),73 we propose to introduce the concept of an Equivalent Aspect Ratio (EAR) by referring to simple cylindrical holes as the reference structure.74 The EAR of a given 3D feature can then be defined as the aspect ratio of a hypothetical cylindrical hole that would require the same reactant exposure dose during an ALD reaction as the feature of interest.

In Fig. 6, the AR and EAR are compared for arrays of holes, trenches,62 and pillars.51 Following expressions for the (E)AR were found:

FIG. 6.

The scheme in the central circle depicts the depth to width ratio (AR) as measured on a cross-section of a 3D feature. The surrounding schemes provide the Equivalent Aspect Ratio (EAR) taking into account the 3D geometry of square holes (a), trenches (b), elongated holes (c), and square pillars (valid for L/w in the range of 5–50 and w/wpillar = 3) (d). The EAR in (d) was determined for molecular flow conditions.

FIG. 6.

The scheme in the central circle depicts the depth to width ratio (AR) as measured on a cross-section of a 3D feature. The surrounding schemes provide the Equivalent Aspect Ratio (EAR) taking into account the 3D geometry of square holes (a), trenches (b), elongated holes (c), and square pillars (valid for L/w in the range of 5–50 and w/wpillar = 3) (d). The EAR in (d) was determined for molecular flow conditions.

Close modal
  • For circular holes: AR = L/w and EAR = L/w.

  • For square holes: AR = L/w and EAR = L/w.

  • For (infinite) trenches, AR = L/w and EAR = L/(2w).

  • For elongated holes, AR = L/w and EAR = (L(w + z))/(2wz), with z being the length of the hole as indicated in Fig. 6.

  • For squares pillars, AR = L/w and EAR=L/(22w) (valid for L/w in the range of 5–50, w/wpillar = 3).

Most expressions follow from Gordon's definition (12) and were further confirmed via Monte Carlo modelling.51 For arrays of pillars, the EAR was not derived from analytical equations, but was determined from Monte Carlo simulations, assuming an initial sticking coefficient of unity (see Sec. V). Comparing holes and trenches with the same AR, the EAR of trenches is a factor of two smaller. For L/w ratios in the range of 5–50 and w/wpillar = 3, we found that the EAR of an array of pillars is a factor of 22 smaller than that for holes. It should be noted that this EAR was determined for molecular flow conditions. Additional calculations would be needed to determine the EAR of an array of pillars for viscous flow conditions and hence confirm whether or not the EAR is a flow regime dependent parameter.

The 3D uniformity of a film is often discussed either in terms of step coverage or conformality (sometimes conformity). The definition of step coverage may vary from reference to reference.

Typically, Step Coverage (SC) is used for the ratio of the film thickness at the bottom of a feature to the film thickness at the top of the feature. Alternatively, it is calculated as the ratio of the film thickness at the side wall to the film thickness at the top [Fig. 7(a)]. Step coverage is typically expressed as percentage. The term is often used for thin films made by PVD75 or (PE)CVD.76 In ALD, the “steps” which may be challenging for PVD and (PE)CVD (e.g., EAR 5:1) are typically coated 100% uniformly, i.e., with 100% SC, and therefore, more demanding (e.g., lateral) test structures are needed for ALD (see Sec. III).

FIG. 7.

(a) Schematic cross-section of a high aspect ratio structure in which the step coverage can be defined as the ratio of the film thickness at the bottom (3) to the film thickness at the top (1) or the ratio of the film thickness at the sidewalls half way into the feature (2) to the film thickness at the top (1). (b) Thickness profile of an ALD process into a high aspect ratio structure. The PD50% and PD80% are indicated.

FIG. 7.

(a) Schematic cross-section of a high aspect ratio structure in which the step coverage can be defined as the ratio of the film thickness at the bottom (3) to the film thickness at the top (1) or the ratio of the film thickness at the sidewalls half way into the feature (2) to the film thickness at the top (1). (b) Thickness profile of an ALD process into a high aspect ratio structure. The PD50% and PD80% are indicated.

Close modal

When analysed with vertical structures, conformality of ALD is often defined in a similar manner as step coverage: ratio of bottom-top or sidewall-top film thickness and is given as percentage.

An alternative way to express the conformality of ALD coatings is via the coated (E)AR. This approach is typically used when the ALD coating did not reach the bottom of the feature and is especially useful for lateral, highly demanding test structures (AR > 50:1). If a thickness profile is experimentally obtained, one determines the penetration depth at which the film thickness equals 50% of the film thickness at the top, which we propose to call the half-thickness-penetration-depth or 50%-thickness-penetration-depth, abbreviated PD50%.77 From the PD50%, one can calculate the coated (E)AR. Alternatively, one can distinguish the coated versus uncoated regions of the feature in cross-sectional electron microscopy or optical images. The transition point is then used to calculate the coated (E)AR. Similar to the PD50%, one can also define the PD80% which stands for the penetration depth at which the film thickness is reduced to 80% of the original film thickness. The concepts of step coverage,78–81 PD50% and PD80%, are illustrated in Fig. 7.

When studying the conformality of a certain ALD process, it is important to use test structures in which gas diffusion occurs according to a flow regime that is relevant for the envisioned application and reactor. Most of the high aspect ratio structures on which ALD is used for applications in micro-electronics,29 batteries,36 fuel cells,31 catalytic surfaces,40 etc., exhibit micrometer- or nanometer-sized dimensions. Note that for porous materials, IUPAC classifies three pore types according to the pore size: micropores have a diameter below 2 nm, mesopores have a diameter in the range of 2 to 50 nm, and macropores have a diameter above 50 nm.82 As shown in Fig. 3, gas diffusion into structures with a characteristic width below ca. 1 μm will be determined by molecular flow when the deposition is performed in traditional flow- (low-vacuum) and pump-type (high-vacuum) ALD reactors (because the mean free path of the reactant molecules will be much larger than the structure width). Therefore, the molecular flow regime is most relevant for the majority of 3D substrates, ALD reactors, and targeted applications although viscous flow applies in some specific cases.

Table I and Fig. 8 overview different types of high aspect ratio test structures that have been used for quantifying the conformality of ALD processes, ordered according to a decreasing size. Many types of structures have been used in mm-μm-nm ranges of feature sizes. We classify them here as vertical structures, lateral structures, and porous materials. These structures often differ in the methods typically used to characterize the film after deposition. Table I includes some references that discuss the fabrication of the specific structures and that introduce a particular characterization approach for determining the thickness/conformality of the deposited ALD coating, as further detailed in Secs. III A–III C.

TABLE I.

Overview of test structures used to quantify the conformality of an ALD process. The labels (a)-(j) correspond to the labeled images in Fig. 8.

StructureCharacterization methodReferences
Macroscopic lateral trenches (a) Ellipsometry/XRR/XRF/EDX 67, 69, 83, and 84  
Capillary tubes (b) Optical microscope 85  
Microscopic lateral trenches (c) Optical and IR microscope/reflectometry 83 and 86–88  
 SEM/EDX  
Micron trenches (d) TEM 89 and 90  
Pillars (e) SEM/EDX 91–94  
Anodized alumina (AAO) (f) EPMA/TEM 65, 95, and 96  
Nano trenches TEM 79 and 97  
Forests of carbon nanotubes (g) SEM/EDX/TEM 98–100  
Opals (h) FE-SEM/TEM 101–104  
Mesoporous thin films (i) XRF/porosimetry/TEM/SIMS 47 and 105–108  
Mesoporous powders (j) TEM/SEM/EDX 109–113  
StructureCharacterization methodReferences
Macroscopic lateral trenches (a) Ellipsometry/XRR/XRF/EDX 67, 69, 83, and 84  
Capillary tubes (b) Optical microscope 85  
Microscopic lateral trenches (c) Optical and IR microscope/reflectometry 83 and 86–88  
 SEM/EDX  
Micron trenches (d) TEM 89 and 90  
Pillars (e) SEM/EDX 91–94  
Anodized alumina (AAO) (f) EPMA/TEM 65, 95, and 96  
Nano trenches TEM 79 and 97  
Forests of carbon nanotubes (g) SEM/EDX/TEM 98–100  
Opals (h) FE-SEM/TEM 101–104  
Mesoporous thin films (i) XRF/porosimetry/TEM/SIMS 47 and 105–108  
Mesoporous powders (j) TEM/SEM/EDX 109–113  
FIG. 8.

Overview of macro-, micro-, and nano-sized test structures to quantify the conformality of ALD processes. (a) Macroscopic lateral trenches.114 (b) Capillary tubes. Adapted with permission from J. S. Becker et al., Chem. Mater. 15(15), 2969–2976 (2003). Copyright 2003 American Chemical Society. (c) Microscopic lateral trenches (LHAR). Reprinted with permission from F. Gao et al., J. Vac. Sci. Technol. A 33(1), 010601:1–010601:5 (2015). Copyright 2015 American Vacuum Society. (d) Trenches (Si). Reproduced with permission from M. Ladanov et al., Nanotechnology 24(37), 375301:1–375301:9 (2013). Copyright 2013 IOP Publishing. (e) Pillars (silicon). (f) AAO. (g) CNT. Reprinted with permission from S. Deng et al., RSC Adv. 4(23), 11648–11653 (2014). Copyright 2014 RSC Publishing. (h) Opals (polystyrene). (i) Mesoporous titania films. Reproduced with permission from J. Dendooven et al., Nanoscale 6(24), 14991–14998 (2014). Copyright 2014 The Royal Society of Chemistry. (j) Mesoporous powders (Zeotile-4). Adapted with permission from S. P. Sree et al., Chem. Mater. 24, 2775–2780 (2012). Copyright 2012 American Chemical Society.

FIG. 8.

Overview of macro-, micro-, and nano-sized test structures to quantify the conformality of ALD processes. (a) Macroscopic lateral trenches.114 (b) Capillary tubes. Adapted with permission from J. S. Becker et al., Chem. Mater. 15(15), 2969–2976 (2003). Copyright 2003 American Chemical Society. (c) Microscopic lateral trenches (LHAR). Reprinted with permission from F. Gao et al., J. Vac. Sci. Technol. A 33(1), 010601:1–010601:5 (2015). Copyright 2015 American Vacuum Society. (d) Trenches (Si). Reproduced with permission from M. Ladanov et al., Nanotechnology 24(37), 375301:1–375301:9 (2013). Copyright 2013 IOP Publishing. (e) Pillars (silicon). (f) AAO. (g) CNT. Reprinted with permission from S. Deng et al., RSC Adv. 4(23), 11648–11653 (2014). Copyright 2014 RSC Publishing. (h) Opals (polystyrene). (i) Mesoporous titania films. Reproduced with permission from J. Dendooven et al., Nanoscale 6(24), 14991–14998 (2014). Copyright 2014 The Royal Society of Chemistry. (j) Mesoporous powders (Zeotile-4). Adapted with permission from S. P. Sree et al., Chem. Mater. 24, 2775–2780 (2012). Copyright 2012 American Chemical Society.

Close modal

Large surface area substrates with vertical features such as arrays of trenches, forests of pillars, carbon nanotubes, or assemblies of pores [e.g., anodized alumina (AAO)] have been used in combination with ALD for applications in fuel cells,31 batteries,35 and supercapacitors.116 Evidently, these substrates can also be used to quantify the conformality of an ALD process. Cross-sectional imaging of the structures [e.g., by scanning electron microscopy (SEM)] allows relatively straightforward visualization of the depth up to which an ALD coating has been deposited. The film thickness can be measured point-by point; accuracy depends on the sample preparation and skills of the electron microscopy operator.

Trenches etched into silicon have most often a width in the range of 100 nm117 to several μm and achieve an EAR of >40:1 [Fig. 8(d)]. Vertical trenches are fabricated with an anisotropic etch process which is designed so that a passivated film is deposited on the sidewalls, while the feature is being etched.118 With the basic Bosch Deep Reactive Ion Etching (DRIE) process, developed in the MEMS industry, one alternates etching and passivation cycles.119 During the passivation cycle, a protective fluorocarbon film is deposited on all the surfaces. This step is followed by an etching step during which an ion bombardment removes the protecting film from all the horizontal surfaces. This technique allows us to achieve trenches with EAR up to 80:1 for widths in the range of 250–800 nm.120 To characterize coated trenches, Gluch et al.89 introduced a TEM lamellae preparation using focused ion beam (FIB) to measure detailed thickness profiles in trenches.

AAO structures can be prepared121 by electrochemical anodization of aluminum films in liquid electrolytes and consist typically of a high density of well-defined parallel and uniform cylindrical pores which are arranged in a hexagonal symmetry with pore diameters between 5 and 300 nm [Fig. 8(f)]. The length of the pores can be controlled from a few tens of nm to a few hundreds of microns. After deposition, cross-sectional SEM is most often used to evaluate the penetration of the ALD coating. Elam et al.65 introduced an alternative approach, where they polished the AAO membrane under a slight angle. In this way, one can obtain cross-sections along the entire length of the pore at different locations in one plane, simplifying the SEM analysis. This measurement technique is illustrated with a thickness profile of a ZnO coating in an AAO structure in Fig. 9(a). Perez et al.95 dissolved the AAO structures and studied the ALD formed nanotubes directly by TEM, avoiding the need for preparing cross-sectional TEM samples from the AAO template. They also introduced an algorithm to automatically determine the wall thickness and diameter of the nanotube as a function of the depth from the TEM images. The thickness profile of a nanotube obtained out of an AAO pore is shown in Fig. 9(b). Recently, Macak and co-workers122 studied the conformality of ALD processes in anodic TiO2 nanotube layers. The closely spaced nanotubes had a diameter of 110 nm and an EAR of 180:1. By depositing the TiO2 nanotube substrate on a quartz crystal that can be mounted in a quartz crystal microbalance (QCM), they were able to perform in-situ QCM measurements during the ALD process. This enabled real-time monitoring of reactant uptake during the ALD reactions. In principle, this approach could be extended to other porous substrates.

FIG. 9.

(a) The thickness profile of a ZnO coating (measured with electron probe micro-analysis) in an AAO structure for several pulse times, as reported by Elam et al.65 Adapted with permission from J. W. Elam et al., Chem. Mater. 15(18), 3507–3517 (2003). Copyright 2003 American Chemical Society. (b) The thickness profile of a HfO2 nanotube that was ALD deposited into an AAO pore, as reported by Perez et al.95 Reprinted with permission from I. Perez et al., Small 4(8), 1223–1232 (2008). Copyright 2008 John Wiley & Sons, Inc.

FIG. 9.

(a) The thickness profile of a ZnO coating (measured with electron probe micro-analysis) in an AAO structure for several pulse times, as reported by Elam et al.65 Adapted with permission from J. W. Elam et al., Chem. Mater. 15(18), 3507–3517 (2003). Copyright 2003 American Chemical Society. (b) The thickness profile of a HfO2 nanotube that was ALD deposited into an AAO pore, as reported by Perez et al.95 Reprinted with permission from I. Perez et al., Small 4(8), 1223–1232 (2008). Copyright 2008 John Wiley & Sons, Inc.

Close modal

Track-etch membranes are another type of micro- or nanoporous materials which are formed by irradiation of polymeric sheets. The diameter of the etched pores can be in the range of a few nm to mm, resulting in an EAR of 10:1–1000:1.123 SEM and cross-sectional TEM are often used to investigate the conformality of the deposited ALD coating in the membranes.124 Arrays of (silicon) pillars93,125 can be either etched or grown through catalyzed chemical vapor deposition [Fig. 8(e)]. To ensure mechanical stability, the pillars often have a diameter and spacing of 1–10 μm and a typically height of 50 μm. Forests of carbon nanotubes (CNTs) can be grown by CVD. Multiwalled CNTs can have a diameter of 10–100 nm and a length up to several micrometers126 [Fig. 8(g)]. To quantify the conformality of an ALD process on pillars or CNTs, EDX mapping is performed on a cross-section to quantify the amount of deposited material along the wall of the pillar or nanotube.92,100

Dedicated lateral test structures have been designed by several groups to enable easy and accurate quantification of the penetration depth and the composition profile of the deposited coating. Often, the goal is to avoid the need of (time-consuming) cross-sectioning and electron microscopy. In the CVD literature, Yang et al.127 used macroscopic lateral structures to analyze the CVD growth of HfO2 thin films. More recently, Shima et al.84 introduced parallel-plate microchannels to study CVD processes. A patterned Si wafer, fabricated by single step etching, is clamped onto a planar Si substrate. In this way, one obtains lateral trench-like features with a microscopic gap size of 1–15 μm (determined by the depth of the Si etching process) and EAR up to 1000:1. The chronological overview below discusses the most important lateral structures that have been used to characterize the conformality of ALD films.

Fused silica capillary tubes with a length of 2 mm and a diameter of 20 μm were introduced as ALD conformality test structures by Becker et al.85 [Fig. 8(b)]. After ALD, the coating on the exterior of the tubes was burned off and the inside was filled with a liquid with a similar refracting index as fused silica. In this way, one could visually determine the penetration depth of the ALD coating on the interior of the tubes using an optical microscope.

Macroscopic lateral structures were introduced by Dendooven et al.67 [Fig. 8(a)] and were created by cutting a rectangular shaped structure (typically 0.5 cm × 2 cm) from a sheet of aluminum foil (thickness of 100–500 μm) and clamping the resulting foil in-between two silicon wafers [Fig. 10(a)]. Because of the design of the structure, exposed regions of the clamped Si wafer are effectively turned into the sidewalls of a lateral trench. By using aluminum foils with different thicknesses and by cutting different shapes, one can easily produce structures with EARs in the range of 1:1–100:1. After ALD deposition, the clamped structure can be disassembled, resulting in two planar Si wafers. The penetration depth of the ALD coating can often be observed with the naked eye. Since the lateral size of the structures is on the order of cm, any technique for characterization of the layer thickness or composition with an intrinsic lateral resolution of the order of 1 mm can be used for obtaining an accurate profile of the thickness and composition of the ALD coating along the sidewalls of the test structure. Quantitative thickness profiles can be obtained, e.g., by ellipsometry (SE), x-ray fluorescence (XRF), or Rutherford backscattering spectroscopy (RBS) mapping of the regions of the Si wafers that constituted the sidewalls in the clamped structure. Not only the film thickness as a function of the depth but also the change in the composition of the deposited film can be monitored. This can be particularly important for complex coatings such as, e.g., ternary oxides or LiPON for 3D battery applications. An advantage of the macroscopic lateral structures is that the small thickness of the deposited film (nms) as compared to the total width of the trench (mms) implies that the EAR of the trench remains essentially constant during deposition, which simplifies, e.g., modelling. The main disadvantage is that their use is limited to pump-type reactors working at high vacuum if the molecular-flow assumption needs to be valid.

Air wedge structures were used by Gabriel et al.83 to quantify the conformality of optical coatings deposited by ALD. These structures consist of two square silicon wafers with a side of 7 cm. The two wafers are in contact along one edge and open at the opposite edge with an air gap of 1560 μm [Fig. 10(b)]. By using such wedge structures, one can simultaneously estimate the penetration depth for a range of EARs (38:1–1410:1).

Musschoot et al.69 extended the method of Dendooven et al. to investigate the penetration of thermal and plasma-enhanced ALD into fibrous materials. They used a hole, made of Teflon (1 cm × 1 cm × 5 cm), and filled it with non-woven polyester. The hole structure was clamped between the substrate holder and a flat Teflon surface. In this way, the ALD reactants could enter the fibrous material from only one side and precursor penetration into the non-woven polyester could be studied in a systematic way.

Puurunen and co-workers86,87 fabricated microscopic lateral high aspect ratio (LHAR) trenches, code named PillarHall structures, with a gap height of 200–1000 nm and EARs up to 12 500:1, using micro-fabrication techniques commonly used for MEMS [Figs. 10(c) and 8(c)]. In recent work, the gap height range was expanded to 100–2000 nm.77 The lateral structures consist of a Si wafer (as bottom surface) and a suspended polysilicon membrane that is locally supported by a network of Si or SiO2 pillars. Elongated openings are etched through the top polysilicon membrane to define the access points for the gas into the lateral structure. After ALD, the film penetration can be investigated non-destructively through the membrane using, e.g., infrared spectroscopy or in some cases using a laboratory microscope. After the removal of the membrane, e.g., by using an adhesive tape, one can measure the penetration depth by microscopy and quantify the thickness of the ALD coating deposited onto the Si wafer (i.e., onto the bottom part of the lateral test structures) by using small-spot size techniques such as reflectometry or SEM/EDX. As indicated in Fig. 3, these LHAR structures with a typical 500 nm gap can be used in flow-type ALD reactors in the molecular flow gas transport regime up to pressures of 1000 Pa.

Recently, Schwille et al.128,129 studied the conformality of ALD in microscopic rectangular cavities with lateral dimensions of 2000 μm, a height of 4.5 μm, and a central access hole with a diameter in the range of 4–60 μm [Fig. 10(d)]. The centrosymmetric nature of the structure slightly complicates the analysis as the equations for trenches do not apply directly because of the different symmetry. These structures resemble those encountered in real MEMS processing.

FIG. 10.

Lateral test structures to quantify the conformality of an ALD process: (a) Macroscopic trenches. Reproduced with permission from J. Dendooven et al. J. Electrochem. Soc. 156(4), P63–P67 (2009). Copyright 2009 The Electrochemical Society. (b) Air wedges. Reprinted with permission from N. T. Gabriel and J. J. Talghader, Appl. Opt. 49(8), 1242–1248 (2010). Copyright 2010 The Optical Society. (c) LHAR structures. Reprinted with permission from F. Gao et al., J. Vac. Sci. Technol. A 33(1), 010601:1–010601:5 (2015). Copyright 2015 American Vacuum Society. (d) A microscopic cavity. Reprinted with permission from M. C. Schwille et al., J. Vac. Sci. Technol. A 35(1), 01B118 (2017). Copyright 2017 American Vacuum Society.

FIG. 10.

Lateral test structures to quantify the conformality of an ALD process: (a) Macroscopic trenches. Reproduced with permission from J. Dendooven et al. J. Electrochem. Soc. 156(4), P63–P67 (2009). Copyright 2009 The Electrochemical Society. (b) Air wedges. Reprinted with permission from N. T. Gabriel and J. J. Talghader, Appl. Opt. 49(8), 1242–1248 (2010). Copyright 2010 The Optical Society. (c) LHAR structures. Reprinted with permission from F. Gao et al., J. Vac. Sci. Technol. A 33(1), 010601:1–010601:5 (2015). Copyright 2015 American Vacuum Society. (d) A microscopic cavity. Reprinted with permission from M. C. Schwille et al., J. Vac. Sci. Technol. A 35(1), 01B118 (2017). Copyright 2017 American Vacuum Society.

Close modal

To tentatively demonstrate that the conformality of an ALD process is indeed determined by the flow type and EAR and not by the absolute dimension, a test was made for this review where a macroscopic lateral test structure reported by Dendooven et al.67 with EAR of 200:1 and LHAR structures reported by Puurunen and co-workers (Generation 1, Ref. 86) were used to compare the quantification of conformality for the same ALD process in the same ALD run. Both substrates were coated in a pump-type reactor during a TMA/H2O process to deposit an Al2O3 film. The process parameters were identical for both substrates: T = 100 °C, PTMA = 0.28 Pa, and PH2O=0.25Pa. During the process, 1000 ALD cycles were applied with the following pulse/pump times: TMA (20 s) - pump (60 s) - H2O (20 s) - pump (60 s). The film thickness was measured with SE in the case of the macroscopic lateral structure, while the coating was visualized using an optical microscope for the microscopic LHAR substrate. For both substrates, a coated EAR of roughly 100:1 was found as can be seen in Fig. 11.

FIG. 11.

A TMA/H2O process was performed on a macroscopic67 (EAR 200:1 / gap 0.1 mm) (a) and microscopic LHAR86 test structure (EAR 200:1 / gap 500 nm) (b) in a pump-type ALD reactor under equal process conditions. Thickness profiles were obtained using ellipsometry (a) and optical microscopy (b). In both cases, a coated EAR of roughly 100:1 was found.

FIG. 11.

A TMA/H2O process was performed on a macroscopic67 (EAR 200:1 / gap 0.1 mm) (a) and microscopic LHAR86 test structure (EAR 200:1 / gap 500 nm) (b) in a pump-type ALD reactor under equal process conditions. Thickness profiles were obtained using ellipsometry (a) and optical microscopy (b). In both cases, a coated EAR of roughly 100:1 was found.

Close modal

Most conformality research has focused on ALD coatings in materials with critical dimensions > 30 nm, e.g., using the above-mentioned Si-based trench structures, AAO, and lateral structures. Fewer studies have focused on ALD coatings in sub-30 nm pores.

George and co-workers investigated ALD of Al2O3, TiO2, and SiO2 in tubular alumina membranes with a pore diameter of 5 nm.47 After each ALD growth reaction, the pore diameter was derived from in situ N2 conductance measurements (assuming molecular flow in the pores). The pore size was smaller after a metal reactant exposure than after the subsequent H2O exposure, in accordance with the replacement of the bulkier metal reactant ligands on the pore walls by the smaller OH groups during the H2O step. The pore diameter was successfully reduced to molecular diameters (estimated in the range of 3–10 Å), demonstrating the potential of ALD in tailoring nanoporous membranes for specific gas separation purposes.

To quantify the penetration of ALD coatings into nanosized pores, Dendooven et al.106,115 developed an approach based on mesoporous SiO2 and TiO2 films that were deposited onto silicon substrates. The mesoporous SiO2 films had randomly ordered channel-like pores with an average diameter of ca. 6.5 nm. The mesoporous titania thin films contained ink-bottle shaped pores [Fig. 8(i)], i.e., spherical cages with a diameter in the range of 4–7 nm connected to each other via smaller pore necks (3–5 nm). The amount of deposited material during the ALD process was monitored using in situ XRF105 and the remaining porosity using in situ grazing incidence small angle x-ray scattering (GISAXS)115 and ellipsometric porosimetry (EP).106 Since the thickness of the deposited film was of the same order as the gap size through which the gas had to enter the pore, the equivalent aspect ratio was not constant, but was gradually increasing as the thickness of the deposited film increased. They demonstrated tuning of the pore size up to near molecular dimensions for both channel-like and ink-bottle shaped mesopores, indicating that the key limiting factor in ALD deposition into nanopores is the diameter of the reactant molecules used during the process, e.g., a TDMAT molecule has a molecular diameter of about 0.7 nm.130 

Opal structures consist mostly of close-packed silica or polystyrene spheres with a diameter of several hundred nm [Fig. 8(h)]. With ALD, one can deposit a coating into the void space between the spheres.101–104 After the deposition, the original spheres can be removed to obtain an inverse opal structure, with potential applications in photonics. The conformality of ALD in these structures can be evaluated by EDX mapping of cross-sections of the opal structure to measure the penetration depth. After the removal of the opal template, cross-sectional SEM can be used to measure the thickness and lateral uniformity of the inverse opal structure.

Besides conformality in mesoporous thin films and opal structures, ALD infiltration in mesoporous powders, including silica gel,109 Zeotile-4111,131 [Fig. 8(j)], metal organic frameworks (MOFs),110,132 and alumina and silica powder with a size of several hundred microns,112,133 has also been a subject of research. ALD functionalization of porous powders is mainly explored for applications in the field of catalysis. The diffusion into the interior portions of these materials is considered to be highly challenging, not only because of the high EAR (few nm wide pores of sometimes several microns in length) but also because of the very large surface areas (up to 2500 m2/g)110 that need to be covered. Because of these large surface areas, the conformality of ALD is governed not only by the diffusion of the molecules but also by the reactant supply.109 

In this section, we aim to provide a systematic overview of experimental data on the conformality of ALD processes. This overview does not aim to include all reported ALD processes but rather concentrates on those reports in which conformality has been investigated in detail. We distinguish three types of ALD processes: thermal, ozone-based, and plasma-enhanced processes. In thermal ALD, the conformality is influenced by the molar mass and reactivity of the ALD reactants as well as by the partial pressures and exposure times. Reactant decomposition (not occurring in theoretical ALD, but sometimes occurring in real processes) and too short purge/evacuation steps may decrease the conformality. For ozone-based and plasma-enhanced ALD, the conformality additionally depends on the recombination of radicals (or ozone) which causes the flux of radicals (ozone) to decrease inside trenches and hence leads to decreased conformality. Therefore, in general, it is expected that better conformality will be achieved for thermal ALD than for ozone-based or plasma-enhanced ALD processes.

Tables II–IV overview experimental results on the conformality of thermal, ozone-based, and plasma-enhanced ALD processes, respectively, as reported in the literature. The indicated EAR has been calculated according to the definition given in Sec. II based on the feature dimensions reported in the corresponding reference. For AAO pores that are accessible from both sides, the tabulated EAR is calculated using half of the pore length. Unless stated otherwise in the table footnotes, the given exposure corresponds to the metal reactant (reactant A) of the ALD process. The value has been calculated from the reactant partial pressures and pulse times reported in the referenced papers (where available). The coated EAR equals the EAR of the structure in the case of complete coverage or is determined by PD50% in the case of incomplete coverage as described in Sec. II and indicated with *. If the results were presented in a thickness profile (film thickness as a function of the depth of the structure), “D” is noted after the coated EAR. Not all fields in the tables could be filled because the process parameters were not always reported. For the PE-ALD processes shown in Table IV, the type of plasma configuration is noted. The influence of the type of configuration will be discussed in Sec. IV C.

TABLE II.

Overview of experimental results on the conformality of thermal ALD processes. The coated EAR equals the EAR of the structure in the case of complete coverage or is determined by PD50% in the case of incomplete coverage and indicated with *. If the results were presented in a thickness profile (film thickness as a function of the depth of the structure), “D” is noted after the coated EAR.

Film (process)SubstrateEART (°C)Exposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3 (TMA/H2O) Macro lateral 100 200 SE 50* (D) 67  
  Trench 300  SEM 134  
  AAO 8.5 200 300 SEM/TEM 8.5 135  
  Nanorods (ZnO)136   300 560 TEM Conformal 99  
  Pillars (Si)137   125  TEM Conformal 93  
  AAO 1750 177  SEM/TEM 1750 96  
  AAO 770 177 4500 SEM 770 65  
  LHAR 10-12500 300  Reflectometry, optical/IR microscopy, SEM 75* (D) 86  
  LHAR 10-12500 300  Reflectometry 130* (D) 77  
  Trench 11   SEM 11 138  
  Macro lateral 75 18 SEM/EDX 2.5* (D) 69  
  Hole 17 200 370 SEM 17 139  
 TiO2 (TiCl4/H2O) Pillars (Si)137   110  TEM Conformal 93  
  AAO 7.5 200  SEM/TEM 7.5 140  
  LHAR 10-12500 110  Reflectometry 90* (D) 77  
      IR microscopy   
  LHAR 10-12500 110  Reflectometry 65* (D) 86  
  AAO 7000 100  SEM/TEM/EDAX 7000 96  
  Opal  70  FE-SEM Conformal 101  
  Opal  80  SEM  141  
 TiO2 (TTIP/H2O) VACNT142   225  SEM (D) 100  
 V2O5 (VOTP/H2O2AAO 7000 100  SEM/TEM/EDAX 7000 96  
 Fe2O3 [Fe2(OtBu)6/H2O] AAO 100 140  SEM/TEM 100 143  
 Fe3O4 [Fe(Cp)2/O2AAO  400  SEM/TEM  144  
 ZnO (DEZ/H2O) AAO 700 177 2000 EPMA 700 (D) 65  
  AAO 5000 177 6 00 000 EPMA 5000 (D) 65  
  Opal  85  SEM  102  
  Trench 250  SEM 90  
  Nanowire (CuO) 145   150  SEM/TEM Conformal 146  
 Y2O3 [(CpCH3)3Y/H2O] Trench 35 300 50 000 TEM 15* 89  
 ZrO [Zr(NMe2)4/H2O] Elliptical holes 36  SEM 36 78  
 ZrO [Zr(NEtMe)4/H2O] Elliptical holes 36  SEM 36 78  
 ZrO [Zr(NEt2)4/H2O] Elliptical holes 36  SEM 36 78  
 RuO2 [Ru(od)3/O2CNT147   300  SEM/TEM Conformal 148  
 SnOx (SnCl4/H2O) Porous silicon 140 500  TEM/SIMS 140 (D) 107  
 HfO2 (HfCl4/H2O) Trench 35 300 2000 TEM 22* 89  
 HfO2 [Hf(NMe2)4/H2O] Holes 36 150 SEM 36 62  
  Elliptical holes 36  SEM 36 78  
 HfO2 [Hf(NEtMe)4/H2O] Elliptical holes 36  SEM 36 78  
 HfO2 [Hf(NEt2)4/H2O] Elliptical holes 36  SEM 36 78  
Nitrides BN (BBr3/NH3AAO 10000 750  SEM 20 149  
 AlN (TMA/NH3Trench 17.5 420  SEM 17.5 150  
 TiN (TiCl4/Zn/NH3Trench 500  SEM 134  
 TaN (TBTDET/NH3Trench 11   SEM 11 138  
 WN [(tBuN)2(Me2N)2W/NH3Silica capillary tube 1000 300 110 Optical microscopy 210 85  
Metals Ru [(iPr-Me-Be)Ru(CHD)/O2Hole 4.6 225, 270, and 310  TEM 4.6 151  
  Hole 25 270  TEM 25152  151  
  Hole 24 220  XTEM 24 153  
 Ru [(EtCp)Ru(DMPD)/O2AAO 20 280  TEM 20 154  
  Hole 17 250  SEM 17155  156  
 Ru [(Et-Be)Ru(CHD)/O2Trench 2.25 225  TEM 2.25 157  
 Ru [(Et-Be)Ru(Et-CHD)/O2Hole 32 225 and 270  XTEM 32 158  
 Ru [Ru(Me-Me2-CHD)2/O2AAO 166 300  TEM 100 159  
 Ru [(EtCp)Ru(Py)/O2Hole 20 275  SEM 20* 160  
  Trench 11   SEM 11 138  
 Ru [Ru(EtCp)2/O2Trench 270  SEM 161  
  AAO 10 300  SEM/TEM 10 162  
 Ru (RuO4/H2Pillars (Si)163  10 100  SEM/EDX Conformal (D) 94  
 Pd [Pd(hfac)2/formalin] AAO 1500 200  SEM/EDX electrical conductivity 1500 164  
 W (Si2H6/WF6AAO 1750 200 60 00 000165  SEM/EDX 1750 (D) 166  
 Ir [Ir(acac)3/O2/H2LHAR 10-12500 250  EDX optical microscopy 20* (D) 87  
 Ir [Ir(acac)3/O2LHAR 10-12500 250  EDX optical microscopy 15* (D) 87  
  Hole 142 300   Ref. 167  168  
  Trench  370  SEM  169  
 Pt (MeCpPtMe3/O2AAO 8.5 250 405 SEM/TEM 8.5 135  
  AAO 150 250  SEM/EDX/SANS 110* (D)170  171  
  AAO 200 300  SEM 94 172  
  Trench 300  TEM 79  
  AAO 200 300  SEM/TEM 50 173  
  Hole 90 300  SEM/TEM 30 173  
 PtIr alloy [MeCpPtMe3/O2/Ir(acac)3/O2AAO 7.1 300  SEM 7.1 172  
  Trench 9.5 300  SEM 9.5 174  
Film (process)SubstrateEART (°C)Exposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3 (TMA/H2O) Macro lateral 100 200 SE 50* (D) 67  
  Trench 300  SEM 134  
  AAO 8.5 200 300 SEM/TEM 8.5 135  
  Nanorods (ZnO)136   300 560 TEM Conformal 99  
  Pillars (Si)137   125  TEM Conformal 93  
  AAO 1750 177  SEM/TEM 1750 96  
  AAO 770 177 4500 SEM 770 65  
  LHAR 10-12500 300  Reflectometry, optical/IR microscopy, SEM 75* (D) 86  
  LHAR 10-12500 300  Reflectometry 130* (D) 77  
  Trench 11   SEM 11 138  
  Macro lateral 75 18 SEM/EDX 2.5* (D) 69  
  Hole 17 200 370 SEM 17 139  
 TiO2 (TiCl4/H2O) Pillars (Si)137   110  TEM Conformal 93  
  AAO 7.5 200  SEM/TEM 7.5 140  
  LHAR 10-12500 110  Reflectometry 90* (D) 77  
      IR microscopy   
  LHAR 10-12500 110  Reflectometry 65* (D) 86  
  AAO 7000 100  SEM/TEM/EDAX 7000 96  
  Opal  70  FE-SEM Conformal 101  
  Opal  80  SEM  141  
 TiO2 (TTIP/H2O) VACNT142   225  SEM (D) 100  
 V2O5 (VOTP/H2O2AAO 7000 100  SEM/TEM/EDAX 7000 96  
 Fe2O3 [Fe2(OtBu)6/H2O] AAO 100 140  SEM/TEM 100 143  
 Fe3O4 [Fe(Cp)2/O2AAO  400  SEM/TEM  144  
 ZnO (DEZ/H2O) AAO 700 177 2000 EPMA 700 (D) 65  
  AAO 5000 177 6 00 000 EPMA 5000 (D) 65  
  Opal  85  SEM  102  
  Trench 250  SEM 90  
  Nanowire (CuO) 145   150  SEM/TEM Conformal 146  
 Y2O3 [(CpCH3)3Y/H2O] Trench 35 300 50 000 TEM 15* 89  
 ZrO [Zr(NMe2)4/H2O] Elliptical holes 36  SEM 36 78  
 ZrO [Zr(NEtMe)4/H2O] Elliptical holes 36  SEM 36 78  
 ZrO [Zr(NEt2)4/H2O] Elliptical holes 36  SEM 36 78  
 RuO2 [Ru(od)3/O2CNT147   300  SEM/TEM Conformal 148  
 SnOx (SnCl4/H2O) Porous silicon 140 500  TEM/SIMS 140 (D) 107  
 HfO2 (HfCl4/H2O) Trench 35 300 2000 TEM 22* 89  
 HfO2 [Hf(NMe2)4/H2O] Holes 36 150 SEM 36 62  
  Elliptical holes 36  SEM 36 78  
 HfO2 [Hf(NEtMe)4/H2O] Elliptical holes 36  SEM 36 78  
 HfO2 [Hf(NEt2)4/H2O] Elliptical holes 36  SEM 36 78  
Nitrides BN (BBr3/NH3AAO 10000 750  SEM 20 149  
 AlN (TMA/NH3Trench 17.5 420  SEM 17.5 150  
 TiN (TiCl4/Zn/NH3Trench 500  SEM 134  
 TaN (TBTDET/NH3Trench 11   SEM 11 138  
 WN [(tBuN)2(Me2N)2W/NH3Silica capillary tube 1000 300 110 Optical microscopy 210 85  
Metals Ru [(iPr-Me-Be)Ru(CHD)/O2Hole 4.6 225, 270, and 310  TEM 4.6 151  
  Hole 25 270  TEM 25152  151  
  Hole 24 220  XTEM 24 153  
 Ru [(EtCp)Ru(DMPD)/O2AAO 20 280  TEM 20 154  
  Hole 17 250  SEM 17155  156  
 Ru [(Et-Be)Ru(CHD)/O2Trench 2.25 225  TEM 2.25 157  
 Ru [(Et-Be)Ru(Et-CHD)/O2Hole 32 225 and 270  XTEM 32 158  
 Ru [Ru(Me-Me2-CHD)2/O2AAO 166 300  TEM 100 159  
 Ru [(EtCp)Ru(Py)/O2Hole 20 275  SEM 20* 160  
  Trench 11   SEM 11 138  
 Ru [Ru(EtCp)2/O2Trench 270  SEM 161  
  AAO 10 300  SEM/TEM 10 162  
 Ru (RuO4/H2Pillars (Si)163  10 100  SEM/EDX Conformal (D) 94  
 Pd [Pd(hfac)2/formalin] AAO 1500 200  SEM/EDX electrical conductivity 1500 164  
 W (Si2H6/WF6AAO 1750 200 60 00 000165  SEM/EDX 1750 (D) 166  
 Ir [Ir(acac)3/O2/H2LHAR 10-12500 250  EDX optical microscopy 20* (D) 87  
 Ir [Ir(acac)3/O2LHAR 10-12500 250  EDX optical microscopy 15* (D) 87  
  Hole 142 300   Ref. 167  168  
  Trench  370  SEM  169  
 Pt (MeCpPtMe3/O2AAO 8.5 250 405 SEM/TEM 8.5 135  
  AAO 150 250  SEM/EDX/SANS 110* (D)170  171  
  AAO 200 300  SEM 94 172  
  Trench 300  TEM 79  
  AAO 200 300  SEM/TEM 50 173  
  Hole 90 300  SEM/TEM 30 173  
 PtIr alloy [MeCpPtMe3/O2/Ir(acac)3/O2AAO 7.1 300  SEM 7.1 172  
  Trench 9.5 300  SEM 9.5 174  
TABLE III.

Overview of the experimental results on the conformality of ozone-based ALD processes. The coated EAR equals the EAR of the structure in the case of complete coverage or is determined by PD50% in the case of incomplete coverage and indicated with *. If the results were presented in a thickness profile (film thickness as a function of the depth of the structure), “D” is noted after the coated EAR.

Film (process)SubstrateEART (°C)Exposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3 (TMA/O3Trench     Ref. 175  176  
 SiO2 ([H2N(CH2)3Si(OCH2CH3)3]/H2O/O3AAO 400   SEM/TEM 400 177  
 TiO2 (TDMAT/O3Hole 20 180  TEM 20* (D) 178  
  CNT179   100 4500 TEM/SEM/EDX  98  
 TiO2 [Cp*Ti(OMe)3/O3Hole 15.3 270  SEM/TEM (D) 180  
 V2O5 (VOTP/O3AAO 100   SEM/EDX 100 150  
  AAO 185 170  SEM/TEM 44 159  
 Fe2O3 [Fe(Cp)2/O3Trench 250  SEM 181  
  Silica aerogel 150 250  SEM/EDX 150 (D) 181  
 Fe2O3 [CpFeC5H4CHN(CH3)2/O3Nanowire (TiO2 230  TEM/EDX Conformal 182  
 Co3O4 (CoCp2/O3Anodisc183  375 167  SEM/EDX 375 (D) 184  
 NiO [Ni(CpEt)2/O3AAO 70 250  TEM/SEM/EDX 70 185  
 NiO (NiCp2/O3AAO 238 300  TEM/SEM 238 186  
 ZnO (DEZ/O3AAO 250 50  SEM/EDX 250 (D) 187  
  Hole188  85  SEM 189  
 HfO2 (TEMAHf/O3Hole 15.3 180  SEM/TEM 9.6* (D) 180  
 IrO2 [Ir(acac)3/O3LHAR 10-12500 185  EDX190 optical microscopy 15* (D) 87  
 PtOx(Pt(acac)2/O3Trench 3.5 120  FE-SEM 3.5 191  
 PtOx (MeCpPtMe3/O3Trench 120  SEM 192  
Metal Ru [Ru(EtCp)2/O3Hole 16 275  TEM 16193  194  
 Ir [Ir(acac)3/O3/H2LHAR 10-12500 185  EDX optical microscopy 45*(D) 87  
  Trench  165  FE-SEM  192  
 Pt (MeCpPtMe3/O3AAO 150 150  SEM/EDX 35* (D) 195  
 Pt (Pt(acac)2/O3AAO 120 150  SEM 50 196  
Film (process)SubstrateEART (°C)Exposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3 (TMA/O3Trench     Ref. 175  176  
 SiO2 ([H2N(CH2)3Si(OCH2CH3)3]/H2O/O3AAO 400   SEM/TEM 400 177  
 TiO2 (TDMAT/O3Hole 20 180  TEM 20* (D) 178  
  CNT179   100 4500 TEM/SEM/EDX  98  
 TiO2 [Cp*Ti(OMe)3/O3Hole 15.3 270  SEM/TEM (D) 180  
 V2O5 (VOTP/O3AAO 100   SEM/EDX 100 150  
  AAO 185 170  SEM/TEM 44 159  
 Fe2O3 [Fe(Cp)2/O3Trench 250  SEM 181  
  Silica aerogel 150 250  SEM/EDX 150 (D) 181  
 Fe2O3 [CpFeC5H4CHN(CH3)2/O3Nanowire (TiO2 230  TEM/EDX Conformal 182  
 Co3O4 (CoCp2/O3Anodisc183  375 167  SEM/EDX 375 (D) 184  
 NiO [Ni(CpEt)2/O3AAO 70 250  TEM/SEM/EDX 70 185  
 NiO (NiCp2/O3AAO 238 300  TEM/SEM 238 186  
 ZnO (DEZ/O3AAO 250 50  SEM/EDX 250 (D) 187  
  Hole188  85  SEM 189  
 HfO2 (TEMAHf/O3Hole 15.3 180  SEM/TEM 9.6* (D) 180  
 IrO2 [Ir(acac)3/O3LHAR 10-12500 185  EDX190 optical microscopy 15* (D) 87  
 PtOx(Pt(acac)2/O3Trench 3.5 120  FE-SEM 3.5 191  
 PtOx (MeCpPtMe3/O3Trench 120  SEM 192  
Metal Ru [Ru(EtCp)2/O3Hole 16 275  TEM 16193  194  
 Ir [Ir(acac)3/O3/H2LHAR 10-12500 185  EDX optical microscopy 45*(D) 87  
  Trench  165  FE-SEM  192  
 Pt (MeCpPtMe3/O3AAO 150 150  SEM/EDX 35* (D) 195  
 Pt (Pt(acac)2/O3AAO 120 150  SEM 50 196  
TABLE IV.

Overview of the experimental results on the conformality of PE-ALD processes. The plasma configuration is indicated as C (capacitive), I (inductive), R (remote), and RE (radical enhanced). The coated EAR equals the EAR of the structure in the case of complete coverage or is determined by PD50% in the case of incomplete coverage and indicated with *. If the results were presented in a thickness profile (film thickness as a function of the depth of the structure), “D” is noted after the coated EAR.

Film (process)SubstrateEART (°C)Plasma configurationExposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3(TMA/O2*) Macro lateral 10 200 RI 27 SE 10 (D) 70  
  Hole 200 RI  SEM 197  
  Trench 13.5 250 RC  SEM 13.5 198  
  Macro lateral199  75 RI EDX 0.75* (D) 69  
  Hole 10 225  30 FE-SEM 10 200  
  Trench 250  TEM 201  
  Trench 15   SEM 15* 202  
 SiO2(3DMAS/O2*) Trench 30 250 RC  SEM 30 198  
 SiO2(H2Si[N(C2H5)2]2/O2*) Trench 15   SEM 15 202  
  Hole 50 75   TEM (D) 203  
 TiO2(TTIP/O2*) Trench 13.5 150 RC  SEM 13.5 198  
  Hole 8.7 225   TEM 8.7 (D) 204  
 Ti(TiCl4/O2*) Trench 4.5 200 RI 75 FE-SEM/FEI 4.5 205  
 HfO2(TEMAHf/O2*) Trench 30 250 RC  SEM 30 198  
 Ta2O5 (Ta(OEt)5/O*Trench 2.5 150-250 RE  SEM 2.5 206  
  Trench 150-250 RE  SEM 206  
 Ta2O5(Ta(OEt)5/O2*) Trench 10 200 RC  SEM 10 198  
Nitrides AlN(TMA/NH3*) Macro lateral 10 200 RI 40207  SE 2.5* (D) 70  
 SiNx(Si2Cl6/NH3*) Trench 2.5 400  TEM 2.5 208  
 SiNx(DTDN2-H2/N2*) Trench 2.75 300   TEM Ref. 209  81  
 TiN(TDMAT/NH3*) Hole 10 250  FE-SEM/AES 10 210  
 TiN(TDMAT/NH3/H2*) Trench 10 180  FE-SEM 10 80  
 TiN(TDMAT/H2*) Hole 10 250  FE-SEM/AES 10 210  
 TiN(TDMAT/N2*) Hole 10 250  FE-SEM/AES 10 210  
  AAO 14 200   FE-SEM 14 211  
 TiN(TiCl4/H2*+N2*) Hole 350  SEM 201  
  Nanotube (TiO2 200  FE-SEM  212  
  Trench 10.5 400 RI  SEM 10.5 213  
 Mo2Nanotrench 2.25 300   XTEM Ref. 214  215  
 (Mo(NtBu)2(StBu)2/H2*)         
 TaN(TBTDET/H2*) Hole 10 260  SEM 10 216  
  Trench    TEM 97  
 TaN (TBTEMAT/H2*Hole 250    217  
 TaNx (PDMAT/H2*Trench 250   TEM 218  
Metals Co((C5H5)2Co/NH3*) Trench 5.5 300 HW 20 FE-SEM 5.5219  220  
 Ni(Ni(Cp)2/H2O/H2*) Hole (TaN) 3.3 165  SEM 3.3 221  
 Cu(Cu(acac)2/H2*) Trench (SiO21.75 85  SEM 1.75 222  
   2.5 85    2.5 222  
   4.5 85    4.5 222  
 Ru(Ru(EtCp)2/NH3*) Trench 290   TEM 2223  97  
  Si,Si/TaN        
 Ag Trench 30 120 RC  FE-SEM 5224  198  
 (Ag(fod)(PEt3)/H2*) (SiO2/TiN)        
 Ag Trench 4.5 140 RE  SEM 4.5 225  
 ((Ag(O2CtBu)(PEt3))/H*        
 Ir(Ir(acac)3/O2*) Hole (elongated)  370  SEM  226  
  Trench  370   SEM  169  
 Pt(MeCpPtMe3/O2*)227  Trench 17 300  SEM 17 (D) 228  
 Ta(TaCl5/H2*) Trench 250  SEM 229  
Film (process)SubstrateEART (°C)Plasma configurationExposure (103 L)Characterization methodCoated EARReferences
Oxides Al2O3(TMA/O2*) Macro lateral 10 200 RI 27 SE 10 (D) 70  
  Hole 200 RI  SEM 197  
  Trench 13.5 250 RC  SEM 13.5 198  
  Macro lateral199  75 RI EDX 0.75* (D) 69  
  Hole 10 225  30 FE-SEM 10 200  
  Trench 250  TEM 201  
  Trench 15   SEM 15* 202  
 SiO2(3DMAS/O2*) Trench 30 250 RC  SEM 30 198  
 SiO2(H2Si[N(C2H5)2]2/O2*) Trench 15   SEM 15 202  
  Hole 50 75   TEM (D) 203  
 TiO2(TTIP/O2*) Trench 13.5 150 RC  SEM 13.5 198  
  Hole 8.7 225   TEM 8.7 (D) 204  
 Ti(TiCl4/O2*) Trench 4.5 200 RI 75 FE-SEM/FEI 4.5 205  
 HfO2(TEMAHf/O2*) Trench 30 250 RC  SEM 30 198  
 Ta2O5 (Ta(OEt)5/O*Trench 2.5 150-250 RE  SEM 2.5 206  
  Trench 150-250 RE  SEM 206  
 Ta2O5(Ta(OEt)5/O2*) Trench 10 200 RC  SEM 10 198  
Nitrides AlN(TMA/NH3*) Macro lateral 10 200 RI 40207  SE 2.5* (D) 70  
 SiNx(Si2Cl6/NH3*) Trench 2.5 400  TEM 2.5 208  
 SiNx(DTDN2-H2/N2*) Trench 2.75 300   TEM Ref. 209  81  
 TiN(TDMAT/NH3*) Hole 10 250  FE-SEM/AES 10 210  
 TiN(TDMAT/NH3/H2*) Trench 10 180  FE-SEM 10 80  
 TiN(TDMAT/H2*) Hole 10 250  FE-SEM/AES 10 210  
 TiN(TDMAT/N2*) Hole 10 250  FE-SEM/AES 10 210  
  AAO 14 200   FE-SEM 14 211  
 TiN(TiCl4/H2*+N2*) Hole 350  SEM 201  
  Nanotube (TiO2 200  FE-SEM  212  
  Trench 10.5 400 RI  SEM 10.5 213  
 Mo2Nanotrench 2.25 300   XTEM Ref. 214  215  
 (Mo(NtBu)2(StBu)2/H2*)         
 TaN(TBTDET/H2*) Hole 10 260  SEM 10 216  
  Trench    TEM 97  
 TaN (TBTEMAT/H2*Hole 250    217  
 TaNx (PDMAT/H2*Trench 250   TEM 218  
Metals Co((C5H5)2Co/NH3*) Trench 5.5 300 HW 20 FE-SEM 5.5219  220  
 Ni(Ni(Cp)2/H2O/H2*) Hole (TaN) 3.3 165  SEM 3.3 221  
 Cu(Cu(acac)2/H2*) Trench (SiO21.75 85  SEM 1.75 222  
   2.5 85    2.5 222  
   4.5 85    4.5 222  
 Ru(Ru(EtCp)2/NH3*) Trench 290   TEM 2223  97  
  Si,Si/TaN        
 Ag Trench 30 120 RC  FE-SEM 5224  198  
 (Ag(fod)(PEt3)/H2*) (SiO2/TiN)        
 Ag Trench 4.5 140 RE  SEM 4.5 225  
 ((Ag(O2CtBu)(PEt3))/H*        
 Ir(Ir(acac)3/O2*) Hole (elongated)  370  SEM  226  
  Trench  370   SEM  169  
 Pt(MeCpPtMe3/O2*)227  Trench 17 300  SEM 17 (D) 228  
 Ta(TaCl5/H2*) Trench 250  SEM 229  
TABLE V.

Sticking probabilities for several ALD reactants and the method how they were derived, as reported in the literature. Updated from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010).

SpeciesDeposited materialMethodT (°C)Sticking probabilityReferences
Al(CH3)3 Al2O3 Monte Carlo model 177 0.001 65  
  Ballistic model 225 0.026 200  
  Semi-Analytical model 200 0.1 67  
  DFT  0.1-0.9 176  
  Continuum model 300 0.00572 77  
  Monte Carlo model 200 0.02 128  
  Sum-frequency generation 100-300 0.002-0.005 237  
  Auger electron spectroscopy 25 0.01 235  
Hf(NEtMe)4 HfO2 Monte Carlo model 180-270 0.03-0.6 180  
Ti(NMe2)4 TiO2 Monte Carlo model 180 0.02 ± 0.005 178  
Ti(OiPr)4 TiO2 Ballistic model 125-225 0.04-0.1 204  
Cp*Ti(OMe)3 TiO2 Monte Carlo model 270 0.01 180  
TiCl4 TiN QCM 25-125 0.006 ± 0.002 236  
  Continuum model 110 0.1 77  
ZnEt2 ZnO Monte Carlo model 177 0.007 65  
SiCl4 SiO2 Monte Carlo model  10–8 65 and 239  
H2Si[N(C2H5)2]2 SiO2 Monte Carlo model 300 3 × 10–5 128  
Zr(NMe2)4 ZrO QCM 200 0.07 78  
Co(C5H5)2 Co Monte Carlo model 300 0.002 220  
H2Al2O3 DFT  0.01-0.1 176  
  Sum-frequency generation 100 0.000001 238  
  Sum-frequency generation 300 0.0001 238  
  Auger electron spectroscopy 25 0.25; 0.009240  235  
O3 Al2O3 DFT  0.001-0.01 176  
O* Al2O3 DFT  0.1-0.9 176  
H* TiN QCM 25-125 0.0003 ± 0.0001 236  
N* TiN QCM 25-125 0.01 ± 0.002 236  
SpeciesDeposited materialMethodT (°C)Sticking probabilityReferences
Al(CH3)3 Al2O3 Monte Carlo model 177 0.001 65  
  Ballistic model 225 0.026 200  
  Semi-Analytical model 200 0.1 67  
  DFT  0.1-0.9 176  
  Continuum model 300 0.00572 77  
  Monte Carlo model 200 0.02 128  
  Sum-frequency generation 100-300 0.002-0.005 237  
  Auger electron spectroscopy 25 0.01 235  
Hf(NEtMe)4 HfO2 Monte Carlo model 180-270 0.03-0.6 180  
Ti(NMe2)4 TiO2 Monte Carlo model 180 0.02 ± 0.005 178  
Ti(OiPr)4 TiO2 Ballistic model 125-225 0.04-0.1 204  
Cp*Ti(OMe)3 TiO2 Monte Carlo model 270 0.01 180  
TiCl4 TiN QCM 25-125 0.006 ± 0.002 236  
  Continuum model 110 0.1 77  
ZnEt2 ZnO Monte Carlo model 177 0.007 65  
SiCl4 SiO2 Monte Carlo model  10–8 65 and 239  
H2Si[N(C2H5)2]2 SiO2 Monte Carlo model 300 3 × 10–5 128  
Zr(NMe2)4 ZrO QCM 200 0.07 78  
Co(C5H5)2 Co Monte Carlo model 300 0.002 220  
H2Al2O3 DFT  0.01-0.1 176  
  Sum-frequency generation 100 0.000001 238  
  Sum-frequency generation 300 0.0001 238  
  Auger electron spectroscopy 25 0.25; 0.009240  235  
O3 Al2O3 DFT  0.001-0.01 176  
O* Al2O3 DFT  0.1-0.9 176  
H* TiN QCM 25-125 0.0003 ± 0.0001 236  
N* TiN QCM 25-125 0.01 ± 0.002 236  
TABLE VI.

Measured recombination probabilities of ozone for various surfaces at different temperatures. Reprinted with permission from H. C. M. Knoops et al., Chem. Mater. 23(9), 2381–2387 (2011). Copyright 2011 American Chemical Society.

SurfaceTemperature (°C)Recombination probability
Al2O3 100-200 <10–6 
ZnO 100 <10–6 
 150 (5 ± 2) × 10–5 
 200 >10–3 
Pt 100 >10–3 
MnOx 100 >10–3 
SurfaceTemperature (°C)Recombination probability
Al2O3 100-200 <10–6 
ZnO 100 <10–6 
 150 (5 ± 2) × 10–5 
 200 >10–3 
Pt 100 >10–3 
MnOx 100 >10–3 
TABLE VII.

Overview of the surface recombination probabilities of O, H, and N atoms on different surfaces as reported in the literature. Updated from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010).

AtomSurfaceRecombination probability rReferences
Alumina 0.0021 250  
  0.0097 ± 0.0019 251  
 Silica 0.0004 252  
 Titania 0.014 ± 0.003 251  
 Iron(III)oxide 0.0052 250  
 Iron(II, III)oxide 0.015 ± 0.003 251  
 Cobalt oxide 0.0049 250  
  0.029 ± 0.006 251  
 Oxidized cobalt 0.085 ± 0.005 253  
 Nickel oxide 0.0089 250  
 Oxidized nickel 0.27 ± 0.04 254  
 Copper (II) oxide 0.043 250  
 Oxidized copper 0.225 253  
 Zinc oxide 0.00044 250  
 Pyrex 0.000045 250  
  0.0020 ± 0.0005 255  
 Stainless steel 0.0702± 0.009 256  
Aluminum 0.0018 257  
 Silicon 0.0016 257  
 Silica 0.00021 ± 0.00003 258  
 Stainless steel 0.0063 257  
Aluminum 0.29 259  
 Oxidized aluminum 0.0018 ± 0.0003 260  
  0.0017 ± 0.0002 261  
 Silicon 0.8 262  
  0.66 263  
 Oxidized silicon 0.0030 ± 0.0003 260  
 Titanium 0.35 259  
 Nickel 0.25 259  
  0.18 ± 0.03 260  
 Copper 0.14 259  
 Pyrex 0.0058 ± 0.0018 259  
  0.00094 ± 0.0004 261  
 Stainless steel 0.030 ± 0.014 260  
 Oxidized stainless steel 0.0022 ± 0.0002 261  
AtomSurfaceRecombination probability rReferences
Alumina 0.0021 250  
  0.0097 ± 0.0019 251  
 Silica 0.0004 252  
 Titania 0.014 ± 0.003 251  
 Iron(III)oxide 0.0052 250  
 Iron(II, III)oxide 0.015 ± 0.003 251  
 Cobalt oxide 0.0049 250  
  0.029 ± 0.006 251  
 Oxidized cobalt 0.085 ± 0.005 253  
 Nickel oxide 0.0089 250  
 Oxidized nickel 0.27 ± 0.04 254  
 Copper (II) oxide 0.043 250  
 Oxidized copper 0.225 253  
 Zinc oxide 0.00044 250  
 Pyrex 0.000045 250  
  0.0020 ± 0.0005 255  
 Stainless steel 0.0702± 0.009 256  
Aluminum 0.0018 257  
 Silicon 0.0016 257  
 Silica 0.00021 ± 0.00003 258  
 Stainless steel 0.0063 257  
Aluminum 0.29 259  
 Oxidized aluminum 0.0018 ± 0.0003 260  
  0.0017 ± 0.0002 261  
 Silicon 0.8 262  
  0.66 263  
 Oxidized silicon 0.0030 ± 0.0003 260  
 Titanium 0.35 259  
 Nickel 0.25 259  
  0.18 ± 0.03 260  
 Copper 0.14 259  
 Pyrex 0.0058 ± 0.0018 259  
  0.00094 ± 0.0004 261  
 Stainless steel 0.030 ± 0.014 260  
 Oxidized stainless steel 0.0022 ± 0.0002 261  

Table II summarizes experiments on the conformality of thermal ALD processes. According to the mean free path of the molecules as discussed in Sec. II, all experiments were performed with the molecular flow regime inside the structures. To conformally coat a substrate with a large EAR, a large dose of reactant molecules is needed because of the increased coated surface area and due to the increased diffusion time of the molecules. For example, Elam et al.65 used an exposure of 6 × 108 L for a conformal ZnO coating of an AAO structure with an EAR of 5000:1. An unusually large exposure time of 10 min, resulting in an exposure of 6 × 109 L, was used to achieve a conformal W coating on an AAO structure with an EAR of 1750:1.166 To reduce the deposition time, one can also increase the reactant partial pressure230 to increase the total exposure.

A majority of studies are done on oxides, nitrides, and metals. Only a few studied the conformality of sulfides,231,232 fluorides,233 and phosphates.92,234 The largest coated EAR by conventional (thermal) ALD is in the range of 5000:1,65 in an extreme case an EAR of 7000:1 is achieved.96 All such ultra-high aspect ratio coatings published in scientific journals are oxides and have so far been made with anodic alumina (AAO) structures. The published processes96 are TiCl4/H2O, DEZ/H2O, and VOTP/H2O2. For the commonly used TMA/H2O process, the record coating so far is EAR 1750:1.96 As seen from Table II, the coated EAR varies greatly for the same process, e.g., 5:1 to 1750:1 for the TMA/H2O process, depending on the test structures and process conditions. Also, the purpose of coating high aspect ratio structures can be different: Rose et al.180 wanted to determine the slope of the thickness profile, and therefore, they on purpose used an unsaturated exposure.

Besides the exposure, high reactivity (often interpreted as high sticking probability) of the reactant molecules is important to achieve a good conformality in structures with a moderate EAR. In Table V, an overview is given of sticking probabilities that have been reported for specific ALD reactants. There is a large variety in reported sticking probabilities, even in values reported for the same reactant molecule. This variety of values is likely partly caused by the large range of methods used to determine the sticking probability. One can use not only theoretical approaches such as density functional theory calculations176 or Monte Carlo modelling128,178 but also several experimental methods to measure the sticking probability, e.g., Auger Electron Spectroscopy235 and QCM measurements;78,236 and more recently, sum frequency generation237,238 has been reported in the literature.

TABLE VIII.

Overview of different modelling works describing the conformality of ALD process, listed in the chronological order.

ReferencesMain modelling approachKey assumptionsSimulation spaceGeometryConclusion
Gobbert282  Ballistic Molecular flow 2D Trench Predict optimal pulse durations 
  Reversible adsorption during first ALD reaction, irreversible reaction during second ALD reaction   Simulation results for both ALD reactions and purge steps 
  Species fluxes constant over small time scales    
Gordon62  Analytical (conductance formulae) Molecular flow Irreversible reactions assumed Cosine distributed re-emission direction Vapor by-products neglected No depletion effects 3D Hole Trench PtKmax2πmkT=1+194a+32a2 Formula to estimate the minimum exposure required for conformal coating of a hole/trench with an aspect ratio a 
Elam65  Monte Carlo Molecular flow Molecule is irreversibly adsorbed if random number < s Re-emission of the molecule over a distance of ±di289 with di the diameter of the pore at the ith position All nanopores identical and molecules are entering the pores from both ends with equal rate 1D AAO Predict minimum exposure for conformal coating sH diffusion limited growth type integrated coverage ∼t1∕2sH reaction limited growth type integrated coverage ∼t 
Neizvestny274  Monte Carlo Only reactant molecules on the substrate are considered, not those in the gaseous phaseBy-products evaporate immediatelyNucleation starts around defects (sticking centers) 3D Porous substrate Growth per cycle stabilization after 2–4 cycles due to the roughness of the surface which increases nucleation 
Kim204  Ballistic Molecular flowIrreversible Langmuir adsorptionStudied 3 re-emission mechanisms: Cosine distributed, random, and specular All reactant molecules chemisorbed on the surface are converted into a solid filmIdeal gas at the hole entrance (flux constant in time) 3D Hole Study step coverage depending on the reactant injection time 
Rose178  Monte Carlo Molecular flowAdsorption if random number < sRe-emission according a random direction 2D Hole Predict thickness profileMethod to determine s 
Dendooven67  Semi-analytical (conductance formulae) Molecular flowIrreversible Langmuir adsorption Cosine distributed re-emission direction Surface reactions occur at a much faster time scale than gas transport into the holeIncoming flux at pore entrance constant in time 1D Cylindrical hole Predict coverage as a function of depthExtension Gordon model: Sticking probability different from unity 
Lee290  Continuum Molecular flowSticking probability of unity“Outer” transport of molecules is much faster than transport into the poresOnly radial diffusionConcentration of reactant molecules is constantIrreversible adsorption 3D Cylindrical, spherical, and planar monoliths with tortuous pores Calculate minimum exposure time for conformal coating. This exposure is largely dependent on the shape of the substrate. 
Dendooven70  Monte Carlo Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction Recombination probability 3D Hole Predict coverage as a function of depth of the feature, for PE-ALD 
Knoops66  Monte Carlo Molecular flow 2D Trench Simulate PE-ALD 
  s,r constant291Cosine distributed re-emission direction   Introduction of recombination limited growth type 
Adomaitis266  Ballistic Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction Reactant in – reactant out = reactant consumedSteady-state fluxes 3D Circular hole Derive reactant transmission probability functions for intra pore feature fluxes 
Musschoot69  Monte Carlo Molecular flowIrreversible Langmuir adsorptionRe-emission according inverse reflection Particles in the medium are at rest, and the moving particle is smallNeglect multiple collisions in a cellTransport equations (transmission, loss, and reflection probability) 1D Hole filled with non-woven polyester Simulate thermal and PE-ALDPredict coverage as function of depth in porous/fibrous substrate 
Yanguas–Gil272  Continuum Molecular flowIrreversible Langmuir adsorptionThe surface coverage changes concurrently with the diffusion process292Time-dependent reaction equation 1D Via Predict saturation exposure times and thickness profilesSuited for high tortuosity structures (high number of wall collisions)Can be extended for viscous flow, 3D,… 
Shimizu220  Monte Carlo Molecular flow 2D Trench Determine sticking probability 
  Irreversible Langmuir adsorption    
  Cosine distributed re-emission direction    
Yazdani100  Continuum(Semi)-analytical Molecular flowQuick equilibrium within and outside the CNT arraysFast distribution of carrier gasAdsorption rate ≫ diffusion rateAnalytic approximation for s = 1Exclude discrete nucleation phase293  3D CNT A larger precursor supply is needed with increasing film thickness due to the increasing diameter of CNTs in combination with a larger surface area, which results in a limited penetration depth and non-uniform thickness 
Keuter294  Continuum Based on the model by Yanguas-Gil272 Including second-order kinetics Transport to the substrate is not considered Pores are not taken individually into account (mean porosity, tortuosity, pore size, and Knudsen diffusion coefficient) Only reaction sites which are not shielded are taken into account 1D Porous substrate Predict thickness profile 
Cremers51  Monte Carlo Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction 3D Square holeSquare pillar The required exposure for conformal coating of an array of pillars is a factor of 2-30 times smaller than an array of holes with equal surface area 
 Analytic Molecular flow    
  Initial sticking coefficient of unity    
  Cosine distributed re-emission direction    
Schwille129  Monte Carlo Molecular and viscous flowCosine distributed/specular re-emission directionCoverage if random number < s 2D centrosymmetric Cavity Predict film thickness in 3D structuresExtraction of the sticking probability of the reactant 
Jin295  Monte Carlo Molecular and viscous flow 3D Nanoparticle agglomerates Predict pulse time for conformal coating 
 Analytic Molecular flow    
Poodt139  Monte Carlo Molecular and viscous flow 2D Trench Predict exposure for 95% coverage 
  Irreversible Langmuir adsorption    
  Simplified diffuse reflection model296     
 Analytic Molecular and viscous flow 3D Hole Predict the saturation dose for complete coverage (only valid in the diffusion limited growth type) 
Ylilammi77  Continuum Molecular flowReversible Langmuir adsorptionIrreversible successive surface reaction 1D LHAR Calculate the thickness profile in high-aspect ratio trenchesExtract kinetic growth information 
ReferencesMain modelling approachKey assumptionsSimulation spaceGeometryConclusion
Gobbert282  Ballistic Molecular flow 2D Trench Predict optimal pulse durations 
  Reversible adsorption during first ALD reaction, irreversible reaction during second ALD reaction   Simulation results for both ALD reactions and purge steps 
  Species fluxes constant over small time scales    
Gordon62  Analytical (conductance formulae) Molecular flow Irreversible reactions assumed Cosine distributed re-emission direction Vapor by-products neglected No depletion effects 3D Hole Trench PtKmax2πmkT=1+194a+32a2 Formula to estimate the minimum exposure required for conformal coating of a hole/trench with an aspect ratio a 
Elam65  Monte Carlo Molecular flow Molecule is irreversibly adsorbed if random number < s Re-emission of the molecule over a distance of ±di289 with di the diameter of the pore at the ith position All nanopores identical and molecules are entering the pores from both ends with equal rate 1D AAO Predict minimum exposure for conformal coating sH diffusion limited growth type integrated coverage ∼t1∕2sH reaction limited growth type integrated coverage ∼t 
Neizvestny274  Monte Carlo Only reactant molecules on the substrate are considered, not those in the gaseous phaseBy-products evaporate immediatelyNucleation starts around defects (sticking centers) 3D Porous substrate Growth per cycle stabilization after 2–4 cycles due to the roughness of the surface which increases nucleation 
Kim204  Ballistic Molecular flowIrreversible Langmuir adsorptionStudied 3 re-emission mechanisms: Cosine distributed, random, and specular All reactant molecules chemisorbed on the surface are converted into a solid filmIdeal gas at the hole entrance (flux constant in time) 3D Hole Study step coverage depending on the reactant injection time 
Rose178  Monte Carlo Molecular flowAdsorption if random number < sRe-emission according a random direction 2D Hole Predict thickness profileMethod to determine s 
Dendooven67  Semi-analytical (conductance formulae) Molecular flowIrreversible Langmuir adsorption Cosine distributed re-emission direction Surface reactions occur at a much faster time scale than gas transport into the holeIncoming flux at pore entrance constant in time 1D Cylindrical hole Predict coverage as a function of depthExtension Gordon model: Sticking probability different from unity 
Lee290  Continuum Molecular flowSticking probability of unity“Outer” transport of molecules is much faster than transport into the poresOnly radial diffusionConcentration of reactant molecules is constantIrreversible adsorption 3D Cylindrical, spherical, and planar monoliths with tortuous pores Calculate minimum exposure time for conformal coating. This exposure is largely dependent on the shape of the substrate. 
Dendooven70  Monte Carlo Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction Recombination probability 3D Hole Predict coverage as a function of depth of the feature, for PE-ALD 
Knoops66  Monte Carlo Molecular flow 2D Trench Simulate PE-ALD 
  s,r constant291Cosine distributed re-emission direction   Introduction of recombination limited growth type 
Adomaitis266  Ballistic Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction Reactant in – reactant out = reactant consumedSteady-state fluxes 3D Circular hole Derive reactant transmission probability functions for intra pore feature fluxes 
Musschoot69  Monte Carlo Molecular flowIrreversible Langmuir adsorptionRe-emission according inverse reflection Particles in the medium are at rest, and the moving particle is smallNeglect multiple collisions in a cellTransport equations (transmission, loss, and reflection probability) 1D Hole filled with non-woven polyester Simulate thermal and PE-ALDPredict coverage as function of depth in porous/fibrous substrate 
Yanguas–Gil272  Continuum Molecular flowIrreversible Langmuir adsorptionThe surface coverage changes concurrently with the diffusion process292Time-dependent reaction equation 1D Via Predict saturation exposure times and thickness profilesSuited for high tortuosity structures (high number of wall collisions)Can be extended for viscous flow, 3D,… 
Shimizu220  Monte Carlo Molecular flow 2D Trench Determine sticking probability 
  Irreversible Langmuir adsorption    
  Cosine distributed re-emission direction    
Yazdani100  Continuum(Semi)-analytical Molecular flowQuick equilibrium within and outside the CNT arraysFast distribution of carrier gasAdsorption rate ≫ diffusion rateAnalytic approximation for s = 1Exclude discrete nucleation phase293  3D CNT A larger precursor supply is needed with increasing film thickness due to the increasing diameter of CNTs in combination with a larger surface area, which results in a limited penetration depth and non-uniform thickness 
Keuter294  Continuum Based on the model by Yanguas-Gil272 Including second-order kinetics Transport to the substrate is not considered Pores are not taken individually into account (mean porosity, tortuosity, pore size, and Knudsen diffusion coefficient) Only reaction sites which are not shielded are taken into account 1D Porous substrate Predict thickness profile 
Cremers51  Monte Carlo Molecular flowIrreversible Langmuir adsorptionCosine distributed re-emission direction 3D Square holeSquare pillar The required exposure for conformal coating of an array of pillars is a factor of 2-30 times smaller than an array of holes with equal surface area 
 Analytic Molecular flow    
  Initial sticking coefficient of unity    
  Cosine distributed re-emission direction    
Schwille129  Monte Carlo Molecular and viscous flowCosine distributed/specular re-emission directionCoverage if random number < s 2D centrosymmetric Cavity Predict film thickness in 3D structuresExtraction of the sticking probability of the reactant 
Jin295  Monte Carlo Molecular and viscous flow 3D Nanoparticle agglomerates Predict pulse time for conformal coating 
 Analytic Molecular flow    
Poodt139  Monte Carlo Molecular and viscous flow 2D Trench Predict exposure for 95% coverage 
  Irreversible Langmuir adsorption    
  Simplified diffuse reflection model296     
 Analytic Molecular and viscous flow 3D Hole Predict the saturation dose for complete coverage (only valid in the diffusion limited growth type) 
Ylilammi77  Continuum Molecular flowReversible Langmuir adsorptionIrreversible successive surface reaction 1D LHAR Calculate the thickness profile in high-aspect ratio trenchesExtract kinetic growth information 

The use of ozone in ALD processes has several advantages. Ozone is a strong oxidizer, whereby some metal reactants react with O3 and not with H2O.5 Another advantage is that at low deposition temperatures, ozone is easier to purge away than the sticky H2O. However, it often proves difficult to achieve a good conformality for ozone-based processes.

In Table III, one observes the best conformality with an ozone-based process for a film growth up to an EAR of 400:1. This result was obtained for a SiO2 three-steps process from H2N(CH2)3Si(OCH2CH3)3 and water and ozone as reactants.177 Also, V2O5 has been deposited conformally in a structure with an EAR of 100:1.78 A conformal ZnO coating was achieved in an AAO structure with EAR of 250:1.187 In general, most of the other ozone-based ALD processes have a relatively small coated EAR in comparison with the corresponding thermal ALD process. While the largest coated EAR of the thermal Al2O3 process was 1750:1,96 for the ozone-based Al2O3 process, the coated EAR was only equal to 20:1.176 This difference in coated EAR between thermal and ozone-based ALD processes is caused by the fact that ozone can thermally decompose in recombination processes on surfaces. The chemical composition of the substrate and the temperature have a large influence on the recombination probability of ozone. Knoops et al.241 studied the recombination probabilities of ozone by measuring the transmission of ozone through high aspect ratio capillaries (EAR 350:1). The recombination probabilities from different processes are shown in Table VI. There are low-loss oxides (silica and alumina), high-loss oxides (MnO2 and Ru2O3), and high-loss noble metal surfaces (Pt). Liu et al.150 reported that during ALD of HfO2 using O3, the decomposition rate increased for increasing substrate temperature, leading to a reduction in the step coverage. This temperature effect is also shown in Table VI for the ZnO process. Besides the temperature and the chemical nature of the surface, other parameters can also influence the recombination coefficient of ozone. For Pt242 and Fe2O3,243 it has been reported that humidity can decrease the decomposition rate of ozone. In these cases, a co-dosing of ozone and H2O could lead to a higher conformality, as earlier suggested by Knoops et al.241 Also, the addition of N2 could in some cases decrease the recombination probability of ozone; however, this can also influence the growth process (increase or decrease the film thickness, depending on the ALD process) and the material properties, e.g., N impurities in the TMA/O3 process.244 

TABLE IX.

Overview of multiscale modelling works describing the conformality of ALD, listed in the chronological order.

ReferencesScaleModelKey assumptionsSpaceGeometryConclusion
Prechtl176  Feature Ballistic B3LYP gradient corrected DFT functional 2D Trench Derive the activation energy of the initial adsorption and s 
 Reactor  Molecular flow Viscous flow 2D Showerhead reactor For low s, depletion on the reactor scale is small; filling time ∼ 1/s 
     For high s, filling time independent of sPredict minimum exposure for conformal coating 
Lankhorst230  FeatureReactor ContinuumContinuum Molecular flowViscous flow 1D3D TrenchBatch reactor Deposition time for conformal coating of trenched wafers ≫ than for flat wafers 
Adomaitis266,270 FeatureReaction ContinuumMonte Carlo Molecular flowMolecular flow 1D2D Nanopore Observe GPC, film density, and surface roughness with varying ALD cycles and reactant dose 
Yanguas-Gil264  FeatureReaction Ballistic Markov chainMolecular flowCosine distributed re-emission directionMultiple reaction channels each with a specific probability 3D Circular pores Track the probabilities as a function of the number of collisionsGeneral expression for the absorption, escape, and effective reaction probability of the featurePredict total exposure required to cover the substrate 
Miyano203  Reactor Continuum Flow rate of the gas below the inlet is uniform over the plane 2D Showerhead reactor Predict the thickness profile in a holePrecursor partial pressure and feeding time can be estimated for a hole with a given a 
 Feature  Molecular flow 2D Hole  
 Reaction  Reversible physisorption, irreversible chemisorption    
ReferencesScaleModelKey assumptionsSpaceGeometryConclusion
Prechtl176  Feature Ballistic B3LYP gradient corrected DFT functional 2D Trench Derive the activation energy of the initial adsorption and s 
 Reactor  Molecular flow Viscous flow 2D Showerhead reactor For low s, depletion on the reactor scale is small; filling time ∼ 1/s 
     For high s, filling time independent of sPredict minimum exposure for conformal coating 
Lankhorst230  FeatureReactor ContinuumContinuum Molecular flowViscous flow 1D3D TrenchBatch reactor Deposition time for conformal coating of trenched wafers ≫ than for flat wafers 
Adomaitis266,270 FeatureReaction ContinuumMonte Carlo Molecular flowMolecular flow 1D2D Nanopore Observe GPC, film density, and surface roughness with varying ALD cycles and reactant dose 
Yanguas-Gil264  FeatureReaction Ballistic Markov chainMolecular flowCosine distributed re-emission directionMultiple reaction channels each with a specific probability 3D Circular pores Track the probabilities as a function of the number of collisionsGeneral expression for the absorption, escape, and effective reaction probability of the featurePredict total exposure required to cover the substrate 
Miyano203  Reactor Continuum Flow rate of the gas below the inlet is uniform over the plane 2D Showerhead reactor Predict the thickness profile in a holePrecursor partial pressure and feeding time can be estimated for a hole with a given a 
 Feature  Molecular flow 2D Hole  
 Reaction  Reversible physisorption, irreversible chemisorption    

In PE-ALD processes, one uses plasma excitation during the reactant exposure step, to create reactive species, such as electrons, ions, and radicals.245 In some cases, PE-ALD offers a higher GPC,197,246,247 higher film density, and lower deposition temperature than those obtained with thermal ALD. A lower deposition temperature can be useful for thermally fragile samples such as biological substrates or polymers.248,249 The main disadvantages of PE-ALD include potential plasma damage to the substrate, more challenges in batch processing for higher throughput, and a limited conformality in high aspect ratio structures due to the recombination of the radicals by collisions with the surface. Note that, during two-particle collisions in the gas phase, recombination will not occur because of preservation of energy and impulse.

When coating deep holes or trenches with PE-ALD, the reactive species undergo multiple wall collisions during which they may be lost through surface recombination before they can reach the surfaces deeper in the hole. The elimination of radicals through recombination on the sidewalls of high aspect ratio structures will inevitably limit the conformality of PE-ALD. This is clear from Table IV, where the highest achieved conformally coated EAR for PE-ALD is only equal to 30:1,198 compared to the much higher conformally coated EAR of thermal ALD (7000:1)96 (Table II).

Table VII lists recombination probabilities r for O, N, and H atoms on various surfaces. The r values span a large range from 0.000094 for the recombination of O atoms on Pyrex to 0.8 for the recombination of H atoms on silicon. In general, higher recombination rates are measured on metallic surfaces, which explains the lower EAR values for PE-ALD of metals listed in Table IV. The largest coated EAR for metals coated with PE-ALD is 17:1, for a Pt coating in a trench structure.228 In contrast, the largest PE-ALD coated EAR for oxides is 30:1 (SiO2 and HfO2).198 Hydrogen radicals have a larger recombination probability than O and N radicals. However, O radicals have larger recombination probabilities on oxide surfaces containing elements with an incomplete d-shell (transition metals, such as Mn, Fe, Co, Ni, and Cu).250 Furthermore, the recombination coefficient will also vary during the plasma pulse, because the surface changes during the pulse, from a surface that is covered with metal reactant ligands to the oxide, nitride, or metal that is deposited. When NH3, N2, and H2 plasmas are used, the high recombination of the N and H radicals may explain why in general it is more difficult to achieve good conformality for these processes in comparison with O2 plasma-based processes.

The conformality of a PE-ALD process depends on the recombination probability and therefore on the type of radical, on the type of material on which it collides (the deposited material), and on the process parameters, such as deposition temperature, gas pressure, and plasma configuration. Varying the gas pressure or the plasma set-up will not only affect the recombination probability but also affect the radical density which has an influence on the conformality.

Dendooven et al.70 used macroscopic test structures to study the influence of the gas pressure, the RF power, the plasma exposure time, and the directionality of the plasma plume on the conformality of the remote PE-ALD of Al2O3 from TMA and O2 plasma and PE-ALD of AlN from TMA and NH3 plasma. Dendooven et al. used a remote inductively coupled plasma ALD reactor where the plasma is located at approximately 50 cm from the substrate. They showed that by increasing the plasma power or the plasma pulse time, the conformality could be improved. For the Al2O3 process using O2 plasma, conformal coatings in holes with an EAR of 10:1 were considered achievable by optimizing the process parameters. The conformality of the AlN process was more limited, and an EAR of 10:1 seemed already out of reach.

Kariniemi et al.198 investigated the conformality of various PE-ALD processes by deposition into microscopic trenches and subsequent characterization by cross-sectional SEM. They showed good conformality of metal oxide coatings deposited in trenches with EARs considerably larger than what had generally been achieved for PE-ALD (up to 30:1). Kariniemi et al. used a capacitively coupled RF plasma operated at two or three orders of magnitude higher pressure configuration, enabling higher radical densities in closer proximity to the substrate. These higher radical densities lead to higher radical fluxes deeper in the trench, enhancing the conformality. In the case of the Ag-PEALD process using H2 plasma, the coating penetrated conformally up to an EAR of 5:1. The low conformality is probably related to the high recombination probability of H radicals on metal surfaces.

It is assumed that in some cases, secondary thermal ALD reactions by the gaseous by-products can lead to an apparently better conformality than could be achieved with a “pure” plasma process. For instance, experimental film thickness profiles obtained for PE-ALD of Al2O3 by Dendooven et al.70 and Musschoot et al.69 could only be reproduced by Monte Carlo simulations if a superposition of two reactions was assumed, i.e., (i) combustion reactions of O radicals with adsorbed TMA molecules near the entrance of the hole resulting in CO2 and H2O as reaction products and (ii) a secondary thermal ALD reaction of these H2O molecules that are diffusing deeper into the structure and react with adsorbed TMA molecules deeper in the hole. On the other hand, Kariniemi et al.198 concluded that the secondary H2O effect played a minor role in their depositions as good conformality was also achieved for the SiO2 process, while the Si-reactant reacts only slowly with H2O. The minor secondary H2O effect might be explained by the difference in radical fluxes inside the high aspect ratio structures. Indeed, for sufficiently large O radical fluxes, as is also the case on planar substrates, the effect of the secondary H2O reaction should be minor as it has to compete with the combustion-like O radical reactions which are likely to occur faster. If the radical flux is low, secondary reactions with the H2O may have a relatively large impact.

A number of models for simulating the conformality of ALD processes, based on different theoretical and numerical approaches, have been developed in recent years. In this section, we aim to provide an overview of the analytical and computational models that are available in the literature. First, a classification for the models is proposed in Sec. V A, while multiscale approaches are addressed in Sec. V B. Next, attention is given to the key assumptions that are used and to how these differ for different models. Finally, typical model output results are discussed. An overview of the reported models focusing on the conformality of ALD is given in Table VIII. In Table IX, an overview of the multiscale models is presented (see also Sec. V B). For each model, the main modelling approach, the key assumptions, simulation space, simulated structures, and the most important conclusions are listed.

1. Analytical models

In 2003, Gordon et al.62 introduced a kinetic model to describe the diffusion and deposition of reactant molecules into holes with aspect ratio a. Gordon et al. defined the aspect ratio as

a=Lp4A,
(13)

with L (m) being the length, p (m) being the perimeter, and A (m2) being the cross-section of the hole.

Gordon et al.62 obtained an analytical expression, based on conductance formulae derived for cylindrical holes, for the exposure required to conformally coat a hole with a certain aspect ratio a

Pt=Kmax2πmkBT(1+194a+32a2).
(14)

In this equation, Kmax is the saturated coverage of the reactant molecule per unit surface area (molecules/m2, in practice, calculated from the GPC value of the process), m (kg) is the mass of the reactant molecules, kB is the Boltzmann constant, and T (K) is the temperature. For large EARs, the required exposure increases approximately quadratically with a. The model assumes that reactant molecules react upon their first collision with an unsaturated part of the substrate walls, implying a sticking probability of unity. Therefore, the model is especially powerful in the diffusion limited growth type, where the exposure time strongly depends on the value of a and much less on the value of the sticking probability (see also Sec. VI B 4 and Fig. 24). Alternatively, in the reaction limited growth type, the model predicts a minimum exposure for the reactant (with a sticking probability of less than one for any real ALD reaction) to coat a structure with a certain a. The predictions of the model agreed with the experimentally determined minimum exposure for coating holes with HfO2 (EAR 36:1).62 

Inspired by the model of Gordon et al., several analytical models on the conformality of ALD processes have been derived. Dendooven et al.67 used a similar approach based on conductance formulae to study the effect of sticking probability on the thickness profile. Ylilammi et al.77 used diffusion equations to study the propagation of ALD growth in narrow channels. Yazdani et al.100 and Cremers et al.51 extended the Gordon model for other geometries being forests of CNTs and arrays of pillars, respectively. Also in these papers, closed-formulae were obtained to calculate the required exposure for conformal coating.

2. Computational models

In addition to the (semi)analytical models, models that require computational effort have been introduced to simulate the conformality of ALD processes. Following the classification suggested by Yanguas-Gil et al.,264 these models can be categorized as ballistic, continuum, and Monte Carlo models.

a. Ballistic models

The derivations for collisionless flow in cylindrical tubes made by Clausing et al.265 in the 1930s provide the basis for the ballistic transport models that are used nowadays. Ballistic models204,266 inherently imply particle transport in the molecular flow regime. One uses the balance of particles to compute fluxes at different locations in the high aspect ratio structure. More specifically, the flux of reactant molecules reaching section i (ϕi) is expressed as the sum of the flux of reactant molecules coming from other sections in the structure (ϕj) and the flux of molecules coming from outside (ϕ0)

Siϕi=jqji(1sjrj)Sjϕj+q0iS0ϕ0,
(15)

with Si being the surface area of the discrete section i and qji and q0i being the probabilities that the molecules coming from section j/0 (outside the trench) can reach section i. sj and rj represent the reaction and surface recombination probabilities. The ballistic transport models used in the ALD literature are based on the earlier work of Cale and co-workers on low-pressure CVD.267,268

b. Continuum models

In continuum models,230,269–273 one uses a diffusion equation to simulate the transport inside a high aspect ratio structure. When the transport of the precursor molecules is described as a diffusion process without an additional flow, the diffusion can be described by Fick's second law

n(t,z)t=D2n(t,z)2z2α(t,z),
(16)

with n(t, z) being the precursor density, z being the axial position along the depth of the high aspect ratio feature, t being the time, D being the diffusion coefficient, and α(t, z) being a loss term representing the adsorption of precursor molecules (chemisorption). Assuming that the cross-sectional area of the pore is independent of the position, particle conservation in a section of the feature reduces Eq. (16) to

n(t,z)tD2n(t,z)z2=s(θ)ΔAsJwall,
(17)

with ΔAs being the surface area per unit volume, s being the reaction probability, θ being the fraction of available sites, and Jwall being the reactant flux per unit area to the walls. The change in surface coverage is given by

θt=s(θ)A0Jwall,
(18)

with A0 being the average surface area of an adsorption site. One can use Eqs. (16) and (17) both in the viscous flow regime and in the molecular regime by using the corresponding diffusion coefficient valid in that regime, as explained in Sec. V C 2. This is an important advantage of continuum models. In addition, the geometry of the substrate is only incorporated via two parameters, the diffusion coefficient and the surface area per unit volume, which makes this type of model suitable for simulation of more complex substrates.

c. Monte Carlo models

In Monte Carlo models,51,65,66,114,178,274 one particle at a time is simulated based on an algorithm, as is exemplified in the flowchart in Fig. 12. Each particle typically represents a certain number of reactant molecules. Following the path of the particle, the intersection of the path and one of the boundary walls of the substrate feature is calculated. A random number x in the range of 0–1 is generated, and a sticking probability s of the reaction is assumed. If x < s, the particle will be adsorbed and another particle will be simulated. Otherwise, the particle will be re-emitted according to a certain type of re-emission mechanism. The simulation stops if a predefined number of particles are simulated or if the predefined integrated coverage of the high aspect ratio structure is achieved.

FIG. 12.

Monte Carlo algorithm used by Cremers et al.51 for the simulation of thermal ALD.

FIG. 12.

Monte Carlo algorithm used by Cremers et al.51 for the simulation of thermal ALD.

Close modal

By modelling the conformality of ALD, one can focus on different length scales, being the reactor, feature, and molecular scale. In Fig. 13, a schematic representation of the different scales in the simulation space is shown. Multiscale approaches aim at combining the transport and/or reactions taking place at two (or more) different length scales.

FIG. 13.

Schematic representation of the reactor scale (for a rotary reactor) (a), the feature scale (b), and the molecular scale (c).

FIG. 13.

Schematic representation of the reactor scale (for a rotary reactor) (a), the feature scale (b), and the molecular scale (c).

Close modal

1. Reactor and feature scale

Several models focus on the feature scale and do not take into account transport at the reactor scale.51,62,66,275 One assumes that the pressure at the opening of the feature or in the reactor, in general, is constant, while, in practice, the pressure will vary during a reactant pulse. For relatively small surface area structures, it can be correct to assume a constant flux of molecules at the feature entry during the ALD reaction. However, if one simulates high surface area materials like porous powders (e.g., SBA-15 powder can have a BET value larger than 300 m2/g)276 or batches of multiple trenched wafers (e.g., 100 V-NAND wafers yield a surface area of 1450 m2 in a batch reactor133), saturation will no longer be limited by the diffusion of the molecules but also by the limited availability of precursor molecules in the reactor.

Lankhorst et al.230 developed a multiscale model in which continuum reactor scale simulations and 1D feature scale simulations of trench structures were combined. They simulated the HfO2 coating of high aspect ratio trenched wafers loaded in a multi-wafer vertical batch reactor. They found that the exposure time required for saturating all trenched wafers with TEMAHf was mainly governed by the timescales corresponding to the following three processes: (1) supply time needed to saturate the gas phase with the reactant, (2) supply time needed to provide sufficient reactant molecules to achieve full coverage, and (3) deposition time needed to coat the trench structures. The latter one can be estimated using the expression of Gordon et al.,62 yielding a value of 6 s for a trench with an EAR of 60:1 and a reactor pressure of 93 Pa. However, the total required exposure time for coating all trenches was found to be a factor of 10 higher, showing that depletion effects in the batch reactor, characterized by large required supply times, have a considerable impact.

Prechtl et al.176 derived the sticking probability by ab initio calculations of the first adsorption step and fed that data as input into a feature scale simulator, coupled with a fluid dynamics based reactor simulator. With this model, they could predict the step coverage and saturation pulse time of an Al2O3 deposition, for a given deposition temperature, ALD tool, trench geometry, and oxygen partial pressure. Prechtl et al.176 performed TMA/H2O depositions in a trench and these experimental results confirmed the validity of their multiscale simulation model.

2. Feature scale and molecular scale

Besides the multiscale approach of the coupling between feature and reactor scale, one can also study the coupling between the transport in the high aspect ratio structure and detailed reaction simulations at the surface. Yanguas-Gil et al.264 used a Markov chain approach to decouple the transport from the complex chemical surface reactions. In a Markov chain approach,277,278 the trajectory of a molecule is represented by a series of transitions between different states. A state represents a certain geometrical position in the simulated feature. Figure 14 shows a schematical presentation of a Markov chain. State i represents the molecule reaching the surface. The molecule can react at point i on the surface according to a certain reaction, e.g., surface reaction pathway Fi1. If the molecule does not react, it can be re-emitted to point j of the surface or it can be re-emitted while leaving the structure (transition to 0). Each transition is characterized with a specific transition probability, e.g., Pij is the probability that the molecule will be re-emitted from position i to position j.

FIG. 14.

Schematic representation of a Markov chain. A molecule in state i can undergo different transitions each with a characteristic transition probability. The molecule can react via a surface reaction pathway or be re-emitted outside the feature (state 0) or re-emitted to position j. Adapted with permission from A. Yanguas-Gil and J. W. Elam, Theor. Chem. Acc. 133, 1465:1–1465:13 (2014). Copyright 2014 Springer.

FIG. 14.

Schematic representation of a Markov chain. A molecule in state i can undergo different transitions each with a characteristic transition probability. The molecule can react via a surface reaction pathway or be re-emitted outside the feature (state 0) or re-emitted to position j. Adapted with permission from A. Yanguas-Gil and J. W. Elam, Theor. Chem. Acc. 133, 1465:1–1465:13 (2014). Copyright 2014 Springer.

Close modal

Adomaitis et al.270 coupled a continuum model to describe the reactant transport in a high aspect ratio nanopore and a Monte Carlo model to describe growth of the ALD film at the molecular scale. This model bridges different timescales: (1) the slower timescale of the film growth over multiple ALD cycles and the resulting evolution in pore geometry (decrease in the pore diameter as a function of pore depth) (min to h) and (2) the faster timescale of the surface reactions taking place during each exposure (ns-ps). Progressive film growth was shown to influence the reactant transport in the nanopore (open at both ends), leading to reactant depletion and reduced growth in the central region of the pore.

In addition to the references listed in Tables VIII and IX and the accompanying discussion in Secs. V A and V B, the authors want to refer the reader to the recent book of Yanguas-Gil,16 providing a more theoretical basis for the different models included here, together with a discussion of growth and transport in the broader context of thin film deposition, also including PVD and CVD concepts. More advanced modeling approaches related to shape evolution during growth and depletion effects at the reactor scale when coating high surface area materials, briefly touched upon above, are also discussed in this book.16 

1. Flow regime: Molecular or viscous flow

A majority of models assume a molecular flow regime where particle-particle interactions can be neglected. This is especially true for the listed ballistic204,266 and Monte-Carlo based models.51,65,66,69,114 As already discussed in Sec. II, one can only obtain a molecular flow regime if the mean free path of the molecules is much larger than the limiting dimensions of the studied features (Kn ≫ 1). An important characteristic of the molecular flow regime is the scale invariance of the reactive transport process.16 The same model to simulate the transport of molecules at a reactor scale can be used to model the transport of molecules inside high aspect ratio structures. However, in the limit of very small pore sizes, i.e., micropores with pore diameter < 2 nm, the diffusion is, in principle, governed by molecular flow but also surface diffusion and the interaction potential between molecules and the walls will affect the reactant transport.16 

Due to the high pressure in atmospheric pressure-type reactors, there is no molecular flow but a viscous flow regime where intermolecular collisions control the diffusion. To be able to describe ALD processes in atmospheric pressure reactors and to get a better insight into the effect of the process parameters, one needs other models which assume a viscous instead of a molecular flow regime. Yanguas-Gil et al.279 developed a continuum formulation model in which the same formulae can be applied for molecular and viscous flow as further detailed. Poodt et al.,139 Schwille et al.,129 and Ylilammi et al.77 developed simulations models, which can be used in both the molecular and viscous flow regimes.

2. Diffusion coefficient in continuum and analytical models

The transport of reactant molecules can be described by a diffusion coefficient. In the molecular flow regime, some models use a diffusion coefficient DKn derived for a cylindrical geometry with pore diameter dp (m)62,67

DKn=dp8kBT9πmp,
(19)

where kB is the Boltzmann constant, T (K) is the temperature, and mp (kg) is the mass of the reactant molecule.

Yazdani et al.100 developed a model to simulate an ALD process on CNTs. The different geometry of the substrate required an adaptation of the diffusion coefficient, which was approximated as

DKn=(1σCNT2r)8kBT9πmp,
(20)

with r (m) being the radius and σCNT (1/m2) being the areal density of the CNTs. Other models assume an approximated diffusion coefficient for other geometries such as square holes or pillars.51 

In the continuum based model of Yanguas-Gil et al.,272 simulations in the viscous flow regime are achieved by replacing DKn by an effective diffusion coefficient D which represents the overall resistance to molecular motion given by

1D=1Dgas+1DKn,
(21)

with DKn being the Knudsen diffusion coefficient (molecular flow contribution) and Dgas being the gas phase diffusion coefficient (viscous flow contribution) given by the Chapman-Enskog approximation.280 

3. Simulation space in Monte Carlo models

Monte Carlo models offer the possibility for modelling complex geometries. However, most models neglect the 3D nature of the experimentally used structures and simulate features in one or two dimensions for simplification and to reduce the computational time. One assumes that the transport of reactant molecules in the structure is largely determined by only one of the dimensions of the cross-section, implying a trench structure instead of a hole structure. Elam et al.65 used a 1D Monte Carlo model, to simulate a TMA/H2O and DEZ/H2O process into an AAO structure. The pores of the AAO structure were modelled as a 1D array with array elements representing the diameter of the nanopore at a specific distance of the opening of the tube. If an element of the array becomes coated, the diameter of the given segment decreased. Other models use a 2D structure.66 However, with a 2D model one cannot distinguish between, e.g., a trench and a cylindrical hole. A 3D MC simulation model was recently developed by Cremers et al.51 They gained insights into the required exposure for conformal coating of an array of pillars versus holes and found that the required exposure to coat an array of pillars is a factor of 2–30 times smaller than the exposure needed to coat an array of holes with equal dimensions. Figure 15 shows a schematic representation of a simulated structure by a 1D, 2D, or 3D model.

FIG. 15.

Simulated hole structure using a 1D (a), 2D (b), or 3D (c) model.

FIG. 15.

Simulated hole structure using a 1D (a), 2D (b), or 3D (c) model.

Close modal

4. Reaction mechanism

In several models, surface dynamics are related to reactant impingement fluxes, which are computed using the kinetic theory of gases. The Langmuir adsorption kinetics are often used as a simplification to model the complex reaction chemistry as was discussed in Sec. II E. Different models67,69,114,241,266,272,279 introduce a sticking probability s to model the conformality of ALD. In Sec. VI, we will further look into the effect of the sticking probability on the thickness profile. Gobbert et al.281,282 combine reversible and irreversible reactions to describe an ALD cycle283 

Ag+*A*,
(22)

where Ag denotes the gaseous reactant A, * is a vacant surface site available for adsorption, and A* stands for chemisorbed reactant A. The irreversible reaction by the second reactant B is described by

A*+BgAB*,
(23)

where Bg stands for the gaseous reactant B, A* is the chemisorbed reactant A, and AB* is the chemisorbed product made from reactants A and B. Equation (22) describes a reversible adsorption of A on a single site, and Eq. (23) describes the irreversible reaction of B with the adsorbed A (known as the Eley-Rideal mechanism).

Yanguas-Gil et al.264 used a Markov chain approach to simulate ALD processes in holes as discussed in Sec. V B. One of the advantages of a Markov chain is that one can easily introduce more complex surface kinetics by adding additional surface kinetic channels with their own transition probability, e.g., extra secondary CVD reaction pathways.

5. Re-emission mechanism

When a particle hits the surface but is not adsorbed, it will be re-emitted (i.e., bounced back) in a certain direction. Three re-emission mechanisms are typically reported in the literature:284 cosine re-emission, specular re-emission, and diffuse elastic re-emission. The different re-emission mechanisms are illustrated in Fig. 16. In the cosine and diffuse elastic re-emission mechanisms, the particle experiences a strong interaction with the surface and loses all information of the incoming trajectory. Following the cosine re-emission mechanism, particles are re-emitted according a cosine distribution. This mechanism was first suggested by Maxwell et al.285 and was later experimentally verified in the work of Clausing286 and Hurlbut et al.287 Knudsen et al.288 used the cosine re-emission mechanism in the derivation of the diffusion coefficient for low-density gases in narrow tubes. This assumption is often used in models of ALD.62,66,67,114,266 When the particle undergoes a diffuse elastic re-emission mechanism, the re-emission direction of colliding particles is random: the particles have an equal probability to be re-emitted in any direction above the surface. The latter mechanism was used by Rose et al.178 in their simulation model. In the specular re-emission mechanism, the re-emission angle is equal to the angle of incidence, and the surface acts as a perfectly reflecting wall. This mechanism is often used for the modelling of CVD processes. Kim et al.204 compared their simulation results with the experimental results of the coverage of a hole with TiO2 to fit several parameters including the re-emission mechanism, and they concluded that the cosine re-emission mechanism produced the best fit to the experimental data.

FIG. 16.

Three re-emission mechanisms which are often used in ALD models: (a) cosine re-emission, (b) diffuse elastic re-emission, and (c) specular re-emission mechanism. Adapted with permission from H. C. Wulu et al., J. Electrochem. Soc. 138(6), 1831–1840 (1991). Copyright 1991 Electrochemical Society.

FIG. 16.

Three re-emission mechanisms which are often used in ALD models: (a) cosine re-emission, (b) diffuse elastic re-emission, and (c) specular re-emission mechanism. Adapted with permission from H. C. Wulu et al., J. Electrochem. Soc. 138(6), 1831–1840 (1991). Copyright 1991 Electrochemical Society.

Close modal

6. Simulating one or multiple ALD cycles

To simplify the simulations, models do not typically simulate the entire ALD cycle, but simulate the first ALD reaction, assuming the second ALD reaction to be fully saturated. A difference between the various models is including or neglecting the film growth. Some models simulate only one ALD reaction.65,66,178,271 The simulated profile is taken as the thickness profile obtained after a multiple cycle deposition. This interpretation is correct as long as the film thickness is small in comparison with the dimensions of the high aspect ratio feature. Otherwise, the EAR experienced by the molecules will increase with an increasing number of ALD cycles, which will have an additional effect on the thickness profile.

Several models include film growth and can simulate multiple ALD cycles.77,100,270,282 In these models, one simulates multiple times the first ALD reaction, assuming the second ALD reaction to be saturated. The film growth is determined by the entire ALD cycle (first reaction + second reaction) and the film thickness equals a multiple of the GPC.

During ALD in a nanoscopic hole, such as an AAO pore, the coating deposited during each ALD cycle decreases the pore diameter, while the EAR increases. This was also demonstrated by Gordon et al.62 with a deposition of HfO2 into patterned holes. The EAR of the holes increased from 36:1 to 43:1, after the deposition. Consequently, for a fixed unsaturated exposure, the Gordon model predicts a decrease in the penetration depth with each ALD cycle deposited. Perez et al.95 showed good agreement between the slope obtained in the thickness profiles for HfO2 ALD in AAO pores and the slope predicted by iteratively applying Gordon's model to a pore that is gradually getting narrower as the ALD process progresses. Similarly, Yazdani et al.100 observed the influence of the film thickness on the exposure required to coat a forest of CNTs. The increasing film thickness leads to an increasing hinderance of the precursor molecules.

In some cases, an additional coating will influence not only the transport of the precursor molecules but also the surface area which has to be coated. When coating nanopores, the pores will gradually shrink, as discussed in Sec. III C. When coating nanowires or CNTs as done by Yazdani et al.,100 the effective surface area which has to be covered will, in fact, increase as the coating thickens.

1. Exposure and EAR

Based on the simulations, one can predict the required exposure to conformally coat a structure with a certain geometry. In addition, systematic simulations can provide valuable insights into the effect of specific simulation parameters on the required exposure time, such as the effect of sticking probability, geometry, and recombination probability.

As an example, Fig. 17 shows the calculated required TMA exposure for holes, trenches, and two arrays of pillars as a function of depth to width ratio using a 3D Monte Carlo model. If we compare the L/w ratios that can be obtained for a given exposure time, for example, for the value indicated by the dashed line in the figure, the L/w ratio that can be coated in a trench is 2 times larger than in a hole, and 22 times larger in a pillar structure with w/wpillar = 3. These factors of 2 and 22 are valid in the plotted range of L/w values, being 5–25. These results confirm the earlier proposed definitions of EAR for trenches and illustrate how simulations can be used to calculate the corresponding EAR of a given structure, as done here for square pillars with w/wpillar = 3. Note that, for square pillars with w/wpillar = 1, it is not possible to derive a general expression for the EAR as a function of L/w that is valid for a large L/w range. Instead, the EAR should be calculated for a particular L/w value. For example, for L/w =25, we find that the required exposure for an array of pillars with w/wpillar = 1 is equal to the required exposure for an array of holes with L/w =10, yielding an EAR of 10 for the array of pillars with L/w =25 and w/wpillar = 1.

FIG. 17.

Absolute TMA exposure required to conformally coat a certain structure with L/w ratio. The structure is either a square hole, a trench or an array of square pillars. L is the height of the structures, and w is the width of the hole or the trench, or is the gap between two adjacent pillars. The width of the square pillars is denoted by wpillar. The data points were obtained using the Monte Carlo model reported by Cremers et al.51 using an initial sticking probability of s0 = 1 and an integrated coverage of 90%.

FIG. 17.

Absolute TMA exposure required to conformally coat a certain structure with L/w ratio. The structure is either a square hole, a trench or an array of square pillars. L is the height of the structures, and w is the width of the hole or the trench, or is the gap between two adjacent pillars. The width of the square pillars is denoted by wpillar. The data points were obtained using the Monte Carlo model reported by Cremers et al.51 using an initial sticking probability of s0 = 1 and an integrated coverage of 90%.

Close modal

2. Thickness profile and sticking coefficient

In addition to knowledge about the required exposure, which is a critical parameter in the experimental optimization of the conformality of ALD processes, fundamental insights into the ALD process can be obtained by simulating detailed thickness profiles as a function of depth in the feature. Figure 18 shows some typical thickness profiles obtained from simulations, published in the literature.66,129,180,272 To evaluate the models, one often compares the simulated thickness profiles with the experimentally obtained thickness profiles. A distinct feature often observed in both experimental and simulated profiles is the slope near the end of the thickness profile. For nanoscopic features, this slope has been partly attributed to the evolution of the EAR of the structure as the deposited film thickness has the same order of magnitude as the width of the structure,95 as already discussed in Sec. V C 6.

FIG. 18.

Thickness profiles of a feature, showing the coverage along the depth of the feature. (a) Trench structure (EAR 50:1) simulated with a MC model. Reprinted with permission from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010). Copyright 2010 The Electrochemical Society. (b) Comparison of experimental and simulated (MC model) thickness profiles of HfO2 films in a cylindrical hole with EAR of 15:1. Reprinted with permission from M. Rose et al., Appl. Surf. Sci. 256(12), 3778–3782 (2010). Copyright 2010 Elsevier. (c) Comparison of experimental and simulated (MC model) thickness profiles of SifO2 films in a centrosymmetric cavity with the diameter of the access hole being 10 μm. Reprinted with permission from M. C. Schwille et al., J. Vac. Sci. Technol. A 35(1), 01B118 (2017). Copyright 2017 American Vacuum Society. (d) Thickness profiles into a structure simulated with a continuum model. Reprinted with permission from A. Yanguas-Gil and J. W. Elam, Chem. Vap. Depos. 18(1-3), 46–52 (2012). Copyright 2012 John Wiley Sons, Inc.

FIG. 18.

Thickness profiles of a feature, showing the coverage along the depth of the feature. (a) Trench structure (EAR 50:1) simulated with a MC model. Reprinted with permission from H. C. M. Knoops et al., J. Electrochem. Soc. 157(12), G241–G249 (2010). Copyright 2010 The Electrochemical Society. (b) Comparison of experimental and simulated (MC model) thickness profiles of HfO2 films in a cylindrical hole with EAR of 15:1. Reprinted with permission from M. Rose et al., Appl. Surf. Sci. 256(12), 3778–3782 (2010). Copyright 2010 Elsevier. (c) Comparison of experimental and simulated (MC model) thickness profiles of SifO2 films in a centrosymmetric cavity with the diameter of the access hole being 10 μm. Reprinted with permission from M. C. Schwille et al., J. Vac. Sci. Technol. A 35(1), 01B118 (2017). Copyright 2017 American Vacuum Society. (d) Thickness profiles into a structure simulated with a continuum model. Reprinted with permission from A. Yanguas-Gil and J. W. Elam, Chem. Vap. Depos. 18(1-3), 46–52 (2012). Copyright 2012 John Wiley Sons, Inc.

Close modal

However, the slope observed, e.g., in the thickness profiles for Al2O3 ALD in macroscopic holes,67 cannot be explained by an increasing EAR during the deposition because the deposited film thickness (∼nm) is negligible compared to the width of the hole (∼100 μm). In this case, the observed slope is interpreted to be related to a sticking probability which is less than one.67 By comparing experimental and simulated thickness profiles, one can determine the initial sticking coefficient as will be explained in more detail in Sec. VI B 3. To give the reader more insights into the effect of the sticking probability on the thickness profiles and, in turn, how an experimentally observed profile can provide information on the chemistry of the ALD process, a systematic simulation study is presented and discussed in Sec. VI.

3. Other output

In addition to exposure and thickness profiles, some models can also explicitly provide information on the pressure profile inside the structure during the deposition, providing information on the reactant molecule distribution and on gas diffusion within the structure.66,114 Yanguas-Gil et al.264 reported statistical information such as the average interaction time, the trajectory of individual reactant molecules, and the average number of wall collisions.

One can obtain fundamental insights into the factors governing the surface chemistry of an ALD process by simulating thickness profiles along the depth of high aspect ratio structures. A schematic figure of a typical experimental thickness profile inside a high aspect ratio feature is shown in Fig. 19. In this review paper, we will focus on the slope at a 50% thickness (PD50%), which we will simply call the slope of the thickness profile. In this section, the effect of the initial sticking coefficient, feature size, and contributions of possible secondary CVD-type reactions on the thickness profile will be investigated by 3D Monte Carlo simulations. The effect of recombination probability has been previously discussed by Knoops et al.66 and will not be considered here.

FIG. 19.

Schematic figure of a typical thickness profile of an ALD process in a high aspect ratio structure, showing the film thickness as a function of the depth of the structure. This profile is characterized by an initial slope and a slope at 50% film thickness (PD50%).

FIG. 19.

Schematic figure of a typical thickness profile of an ALD process in a high aspect ratio structure, showing the film thickness as a function of the depth of the structure. This profile is characterized by an initial slope and a slope at 50% film thickness (PD50%).

Close modal

The calculated deposition profiles are obtained through 3D Monte Carlo simulations51 and describe the coverage of the ALD film along the hole wall after the first reaction of the ALD cycle. All simulations were performed for a square hole with an EAR of 50:1 (width of 10 and depth of 500 distance units). To investigate the evolution of the sidewall coverage as a function of time during the ALD process, several thickness profiles are given for different exposure doses. The exposure doses are expressed in terms of the normalized exposure which is the exposure divided by the exposure required to saturate a flat surface. In Fig. 20, an overview is presented of the thickness profiles for theoretical saturation-based ALD processes. It was chosen to plot the thickness profiles as a function of EAR as this, in principle, would allow a better comparison of the profile shape, and the slope at PD50%(dθd(EAR)) with earlier published results. For each case, two profiles are shown, calculated for open-ended structures and structures terminated by a bottom surface. In both cases, the reactant molecules can only enter the structure from the top, as is illustrated in Fig. 21. The effect of the terminating bottom surface will be discussed for all cases in Sec. VI B 5.

FIG. 20.

Thickness profiles of a hole structure with EAR of 50:1, in the case of a theoretical ALD process, based on saturating, irreversible reactions. The schematic s-plots represent the sticking probability s as a function of the surface coverage θ. The analytical derived thickness profile according to Gordon et al.62 is shown in (a). Simulated thickness profiles are shown, assuming the irreversible Langmuir-type adsorption with varying initial sticking coefficients s0 = 1 (b), 0.1 (c), 0.01 (d), and 0.001 (e). For each case, the thickness profiles are shown for a hole structure without (middle) or with (right) a terminating bottom surface. The normalized exposure required to obtain the different thickness profiles is given by black line: 700, blue line: 1400, red line: 2100, and green line: 2800.

FIG. 20.

Thickness profiles of a hole structure with EAR of 50:1, in the case of a theoretical ALD process, based on saturating, irreversible reactions. The schematic s-plots represent the sticking probability s as a function of the surface coverage θ. The analytical derived thickness profile according to Gordon et al.62 is shown in (a). Simulated thickness profiles are shown, assuming the irreversible Langmuir-type adsorption with varying initial sticking coefficients s0 = 1 (b), 0.1 (c), 0.01 (d), and 0.001 (e). For each case, the thickness profiles are shown for a hole structure without (middle) or with (right) a terminating bottom surface. The normalized exposure required to obtain the different thickness profiles is given by black line: 700, blue line: 1400, red line: 2100, and green line: 2800.

Close modal
FIG. 21.

Schematic figure of the simulated square holes with (a) and without a bottom (b). In both cases, the reactant molecules can only enter the structure from the top, as indicated by the arrows.

FIG. 21.

Schematic figure of the simulated square holes with (a) and without a bottom (b). In both cases, the reactant molecules can only enter the structure from the top, as indicated by the arrows.

Close modal

1. Model of Gordon et al.: Sticking probability of unity

Assuming a constant sticking probability of unity, the thickness profile consists of a fully covered part and an almost uncovered part, separated by a distinct and abrupt front (a step function). Gordon et al.62 suggested that the top part of the trench, where all active surface sites have been saturated, can be considered as a “vacuum tube” because impinging molecules simply bounce back. The bottom part of the trench, which presents a surface covered with reactive sites, acts as a “vacuum pump” because all impinging molecules will stick to the wall. Based on this representation of the problem as a vacuum system, Gordon et al. derived an expression to calculate the exposure required to saturate a hole with given dimension, as already discussed in Sec. V A 1. For an unsaturated exposure, the Gordon model predicts complete coverage of the pore walls up to a depth l

l=4w3(1+38(PtKmax2πmkBT)1).
(24)

Step profiles calculated with Eq. (24) are shown in Fig. 20(a).

2. Irreversible Langmuirian adsorption: Influence of sticking probability on the thickness profile

Thickness profiles simulated assuming Langmuir-type irreversible adsorption with different values of the initial sticking coefficient are shown in Figs. 20(b)–20(e). If one introduces Langmuir's adsorption assumption to describe the sticking probability, (s = s0(1 – θ)), still assuming the initial sticking probability to be one, one observes a more gentle slope in the thickness profile compared to the step function of Gordon et al.62 With the decrease in s0, the profile becomes smoother. For very low initial sticking probabilities, the unsaturated thickness profiles do not necessarily show the initial plateau as shown in Fig. 20(e). This situation corresponds to the sketch in Fig. 4(b). Similar thickness profiles were also observed in the simulations of Rose et al.,178 Dendooven et al.,67 and Knoops et al.66 

3. Determining the initial sticking coefficient through comparing simulation with experiment

By comparing simulated thickness profiles with experimentally obtained profiles, one could quantify the initial sticking coefficient of a given ALD reactant. This approach was suggested by Rose et al.178 who obtained a value of 0.02 ± 0.005 for TDMAT on TiO2. Shimizu et al.220 obtained a sticking probability of 0.002 for a (C5H5)2Co reactant. However, one should realize that a high density of data points along the depth profile, and especially in the slope region, are needed in order to make a fair conclusion about the sticking probability, as illustrated in Fig. 22. In this respect, the recent development in experimental test structures and analysis methods to extract depth profiles, e.g., by the microscopic LHAR structures86 or the recently introduced methods by Schwille et al.,129 might allow for a more detailed investigation of the initial sticking coefficient. By comparing experimental and simulation results, Bartha and co-workers could extract an initial sticking coefficient of 2 × 10−2 for TMA and 3 × 10−5 for the BDEAS reactant.128 Ylilammi et al.77 compared experimental results, obtained using the microscopic LHAR structures, with simulation results, obtained using their diffusion model, to extract an initial sticking coefficient of 0.00572 for TMA.

FIG. 22.

Experimental thickness profile obtained using spectroscopic ellipsometry for Al2O3 ALD (TMA/H2O process) in a macroscopic rectangular hole with EAR of 100 and a cross-section of 0.1 mm × 5 mm. The deposition was done in a pump-type reactor with a partial pressure of TMA of 0.27 Pa, a TMA pulse time of 4 s, and a deposition temperature of 200 °C. The simulated profiles were calculated using the MC model of Dendooven et al.114 and scaled to the film thickness measured in the first experimental data point.

FIG. 22.

Experimental thickness profile obtained using spectroscopic ellipsometry for Al2O3 ALD (TMA/H2O process) in a macroscopic rectangular hole with EAR of 100 and a cross-section of 0.1 mm × 5 mm. The deposition was done in a pump-type reactor with a partial pressure of TMA of 0.27 Pa, a TMA pulse time of 4 s, and a deposition temperature of 200 °C. The simulated profiles were calculated using the MC model of Dendooven et al.114 and scaled to the film thickness measured in the first experimental data point.

Close modal

4. Influence of the initial sticking coefficient on the required exposure

In addition to the influence of sticking probability on the thickness profile, it is of interest to understand the effect of sticking probability on the reactant exposure required for creating a conformal coating up to a certain EAR. In Fig. 23, the exposure is shown as a function of the EAR, simulated by the Monte Carlo code of Cremers et al.51 Similar simulation results were earlier published by Knoops et al.66 For small EAR (up to EAR 30:1), one observes an increasing exposure for decreasing s0 in the range of 1 to 0.01. With the increase in EAR, the exposure becomes independent of s0. As already discussed in Sec. II, these two behaviors correspond to the reaction limited (low EAR) and diffusion limited (high EAR) growth types, respectively. The exposure required to obtain a fully covered trench with high EAR (100:1) is equal for different initial sticking coefficients, in the range of 1–0.01; however, the thickness profile for unsaturated doses differs with the initial sticking coefficient due to differences in the ALD reaction kinetics. Figure 23 shows the difference in the thickness profile for an unsaturated coating of a trench structure with EAR of 100:1 and for an initial sticking coefficient of 1 and 0.01, respectively.

FIG. 23.

Absolute exposure for a TMA/H2O process as a function of the EAR for a trench structure (with bottom) and s0 = 1, 0.1, and 0.01 and an integrated coverage of 90% obtained from 3D Monte Carlo simulations. The inset shows the difference for s0 = 1 and s0 = 0.01 in the thickness profile for an EAR of 100:1 and an integrated coverage of 49%.

FIG. 23.

Absolute exposure for a TMA/H2O process as a function of the EAR for a trench structure (with bottom) and s0 = 1, 0.1, and 0.01 and an integrated coverage of 90% obtained from 3D Monte Carlo simulations. The inset shows the difference for s0 = 1 and s0 = 0.01 in the thickness profile for an EAR of 100:1 and an integrated coverage of 49%.

Close modal

5. Influence of the bottom of the feature

The existence of a bottom in the structure is expected to have an impact on the thickness profile for an unsaturated exposure dose. In Monte Carlo simulations, we observe that for each case listed in Fig. 20, in general, the bottom of the structure is covered faster than the adjacent walls. This phenomenon of a faster coverage of the bottom of the structure has been observed in other Monte Carlo simulations51,66 and was also reported in ballistic models264,266 which can be explained by the contribution of the direct pore-opening to pore-bottom flux, leading to a higher coverage of the bottom in comparison with the pore walls.

In a typical flux-controlled CVD process, film growth depends on the local gas flux and lacks the characteristic self-limited behavior of ALD. To simulate the contribution of a CVD-type process, the same simulation program was used for the ALD processes, omitting the limit on the number of adsorbed reactant molecules per unit area and with an adapted sticking probability s. We reduce the chemistry of the CVD process to one parameter preaction which represents the reaction probability and which is independent of the local coverage θ. In the schemes in Fig. 24, the CVD contribution is represented by the orange curve. The ALD contribution during the process is similar as in the theoretical ALD case, assuming the irreversible Langmuir adsorption (blue curve). The total probability that a reactant molecule sticks on the surface (green curve) is given by the sum of the probability for ALD reaction and preaction of the irreversible CVD process, leading to

s=s0(1θ)+preaction.
(25)
FIG. 24.

Simulated thickness profile of a hole structure with EAR of 50:1, in the case of an ALD process with a CVD contribution, based on irreversible reactions. The schematic s-plots represent the sticking probability s as a function of the surface coverage θ assuming the irreversible Langmuir-type adsorption with varying initial sticking coefficients s0 = 0.1 (a) and 0.01 (b) and reaction probability of the CVD contribution preaction = 0.0002. For each case, the deposition profiles are shown for a hole structure without (middle) or with (right) a terminating bottom surface. The normalized exposure required to obtain the different thickness profiles is given by black line: 700, blue line: 1400, red line: 2100, and green line: 2800.

FIG. 24.

Simulated thickness profile of a hole structure with EAR of 50:1, in the case of an ALD process with a CVD contribution, based on irreversible reactions. The schematic s-plots represent the sticking probability s as a function of the surface coverage θ assuming the irreversible Langmuir-type adsorption with varying initial sticking coefficients s0 = 0.1 (a) and 0.01 (b) and reaction probability of the CVD contribution preaction = 0.0002. For each case, the deposition profiles are shown for a hole structure without (middle) or with (right) a terminating bottom surface. The normalized exposure required to obtain the different thickness profiles is given by black line: 700, blue line: 1400, red line: 2100, and green line: 2800.

Close modal

In Fig. 24, we show two cases of an ALD reaction with a small CVD contribution described with a preaction of 0.0002. In (a) and (b), the initial sticking probability for the ALD reaction is considered 0.1 and 0.01 so that the simulation results can be compared to the ideal ALD cases in Figs. 20(c) and 20(d), respectively. The main impact of the CVD contribution can be observed in the thickness profile near the feature opening. A coverage degree of more than 100% can be observed at the entrance of the structure, with a value of ca. 160% for the highest simulated exposure. As expected due to the flux controlled nature of the CVD contribution, the coverage degree increases with exposure time, surpassing the value of 100%, meaning that more than one saturated ALD layer is deposited. Instead of the initial plateau typically observed in the ideal ALD simulations for s0 above 0.01 (Fig. 20), the CVD contribution gives rise to a decreasing contribution because the flux decreases inside the feature. With the increasing exposure time, the slope of this initial part of the thickness profile increases, ultimately leading to clogging of the feature mouth. This first part of the thickness profile is in agreement with the classical CVD profile.297 Deeper in the trench, where the coverage drops below 100%, we recognize the characteristics of the typical ALD profile, with a marked slope. This slope decreases with the decrease in the initial sticking probability, as for the ideal ALD case, and the slope and its position (PD50%) are only slightly affected by the CVD component. The PD50% is a bit lower because the reactant molecules that are consumed in the CVD reactions near the feature entrance are no longer available to contribute to deposition deeper in the structure. The effect of the presence of a feature bottom is comparable to the ideal ALD case.

To the best our knowledge, simulated profiles including a CVD contribution have not been studied in detail before nor have they been compared to experimental data. A more extended study of simulations could be instructive on how even a minor CVD component can impact the conformality of an ALD process. Moreover, comparing those thickness profiles with experimental thickness profiles can potentially give a hint on the possible contribution of a CVD-like component in the ALD process. To this end, it would also be useful to extract experimental thickness profiles for processes where intentionally a CVD component is present, e.g., by selecting a deposition temperature slightly above the decomposition temperature of the metal ALD reactant.

The position of the slope and the gradient of the slope of the simulated profiles are often in good agreement with the experimental results.67,178 However, there are also examples of experimental data which cannot yet be explained by the simulations. For example, there are experimental profiles in which no initial plateau is observed114 (e.g., experimental profile in Fig. 22), or which show a more gradual decrease lacking a well-defined slope,69 or a long tail following the slope.69 Although irreversible Langmuir adsorption can often satisfactorily describe the thickness profile, real processes are more complex, and the assumptions of the Langmuir model likely oversimplify the reactions. Moreover, additional reactions might contribute in real ALD processes:

  • The more complex real surface chemistry may also include reversible reactions, which would affect the observed thickness profile. Then, some surface groups left behind after one reaction of the ALD process may desorb during the purging/pumping step, resulting in less reaction sites for the next ALD reaction. Indeed, when coating demanding 3D structures, the purging/pumping time is typically prolonged to allow excess reactants and reaction by-products to diffuse out of the structure. This prolonged evacuation time may affect the possible desorption of surface species.298 

  • Real gas-solid reactions used in ALD may involve multiple surface reaction pathways that occur simultaneously or consequently. For example, for the TMA/H2O process, the existence of multiple reaction pathways has been proposed on the basis of experimental evidence.5 Simulations have been made264 to illustrate possible coverage profiles with a theoretical two-site model with different reaction probabilities.

  • With this Monte Carlo code, the simulations are limited to the first reactant exposure of the ALD process, assuming the second reaction to be saturated. However, in the experimental cases, it is possible that both reactions are unsaturated and both limit the conformal nature of the coating. In this case, both reactions will contribute in their own particular way to the profile, which may lead to more complex profiles.

  • As already discussed in Sec. V C 6, by deposition of more cycles, the EAR of the coated structure increases and the deposited film gradually limits the diffusion on molecules. As a consequence, the steepness of the slope of the thickness profile will decrease.

  • In the growth of composite films by ALD, etching reactions of one of the reactants can occur.275 This etching effect may be dependent on the flux of one of the reactants and may therefore have an influence on the corresponding thickness profile of the ALD process.

  • Metal ALD on oxide supports usually suffers from nucleation difficulties. Islands form at the start of the process, which in turn act as catalysts for the ALD reactions, speeding up the growth. However, in 3D structures, due to differences in local reactant pressure along the feature, the nucleation process will likely be delayed deeper in the trench. In that way, the catalytic enhancement of the ALD growth will be affected and as such also the conformality. Also for applications in catalysis where one is mainly interested in nanoparticles grown by ALD, the diffusional limitations may cause an uneven nucleation over a 3D porous structure.

  • Gaseous by-products (e.g., HCl) may not be inert, but they can occupy the same sites as the reactant.273 Therefore, less sites will be available for the reactant molecules, potentially leading to a decrease of the GPC deeper in the structure.

In this review, an overview was given on the analysis and modelling of the conformality of ALD processes. Vertical, lateral, and (meso)porous substrates can be used to quantify the conformality of ALD processes. In general, a higher conformality is obtained for substrates coated with thermal ALD processes than with energy-assisted processes, such as plasma-enhanced and ozone-based ALD where surface recombination inevitably leads to a decrease in the impingement flux of radical/ozone species at an increasing penetration depth. To gain more insight into the process parameters influencing the conformality of ALD, several models (classified as analytical, ballistic, continuum, Monte Carlo) have been reported in the literature. Models can predict the exposure required to conformally coat a certain substrate and can simulate detailed thickness profiles as a function of the penetration depth. These profiles for unsaturated exposures are characterized by a slope, the steepness of which increases with the increasing initial sticking coefficient. The steepness of the slope can also be influenced by an increasing aspect ratio of the coated feature during deposition due to the comparable dimensions of the feature and the film thickness. Besides ALD, also other reactions can influence the profile. CVD type contributions lead to growth beyond the ALD GPC and clogging at the entrance of the feature. An interesting potential direction of future work concerns investigating how more complex effects, such as etching, nucleation, and undersaturation, contribute to the thickness profile.

To define the achieved conformality, in the literature, one often focuses on the conformality as percentage or on the penetration depth of the deposited film along the high aspect ratio substrate. However, also the properties of the film could change along the depth of the structure. It would be interesting to extend the work on conformality to the observation of differences in the composition, crystallinity, morphology, and functional properties (e.g., dielectric constant, electronic conductivity, or Li ion conductivity) of the deposited coating.

Experiments on the conformality of ALD films have been performed on a variety of test structures for which often the height to width ratio (AR) is reported. This diversity of substrates and the lack of a standard definition of a geometry-independent aspect ratio often complicate a direct comparison of the reported results. In this article, we propose a geometry independent equivalent aspect ratio (EAR), to facilitate comparison. For benchmarking purposes, it would be interesting to investigate a specific process with different types of conformality test structures and cross-compare the results. In the future, it might be beneficial if a consensus could grow within the ALD community on the use of standardized test structures which could be used to validate conformality across a wide range of ALD reactor types, pressure ranges, and ALD processes.

Professor Christophe Detavernier, Ghent University, and Dr. Markku Ylilammi, VTT, are thanked for the many helpful discussions. V.C. acknowledges financial support from the Strategic Initiative Materials in Flanders (SIM, SBO-FUNC project). R.L.P. acknowledges funding from the Finnish Centre of Excellence on Atomic Layer Deposition by the Academy of Finland and the PillarHall project by Tekes, currently Business Finland. J.D. acknowledges the Research Foundation Flanders (FWO-Vlaanderen) for a postdoctoral fellowship. All the authors acknowledge the HERALD COST network.

*

Vacant surface site

3DMAS

Tris(dimethylamino)silane

a

Aspect ratio according to Gordon et al.62 

α(t, z)

Loss term representing the adsorption of precursor molecules

A

Cross-sectional area

A*

Chemisorbed reactant A

Ag

Gaseous reactant A

ΔAs

Specific surface area (area per unit volume)

A0

Average surface area of an adsorption site

AAO

Anodized Aluminum Oxide

acac

Acetylacetone

AES

Auger Electron Spectroscopy

ALD

Atomic Layer Deposition

AP-type reactor

Atmospheric pressure type reactor

AR

Aspect ratio

B3LYP

Becke, three-parameter, Lee-Yang-Parr

Be

Benzene

CHD

Cyclohexadienyl

CNT

Carbon Nanotube

Cp

Cyclopentadienyl

CVD

Chemical vapor deposition

d

Molecule diameter

dp

Pore diameter

D

Diffusion coefficient

Dgas

Gas phase diffusion coefficient (viscous flow contribution)

DKn

Knudsen diffusion coefficient

DEZ

Diethylzinc

DMPD

Dimethylpentadienyl

DRAM

Dynamic random-access memory

DRIE

Deep Reactive Ion Etching

EAR

Equivalent aspect ratio

EDX

Energy-dispersive x-ray spectroscopy

EPMA

Electron probe microanalysis

EP

Ellipsometric Porosimetry

ϕ0

Flux of reactant molecules coming from outside

ϕi

Flux of reactant molecules reaching section i

(FE)-SEM

(Field emission)-scanning electron microscope

FIB

Focused Ion Beam

fod

6,6,7,7,8,8,8-heptafluoro-2,2-dimethyl-3,5-octanedionate

Gg

Gaseous reaction by-product

GISAXS

Grazing Incidence Small Angle X-ray Scattering

GPC

Growth per cycle

HAR

High aspect ratio

hfac

Hexafluoroacetylacetone

IR

Infrared

IUPAC

International Union of Pure and Applied Chemistry

Jwall

Reactant flux per unit area to the walls

kads

Adsorption rate constant

kB

Boltzmann constant

kdes

Desorption rate constant

Kmax

Saturated coverage of the reactant molecule per unit surface area

Kn

Knudsen number

λ

Mean free path

λ0,i

Specific scattering length of molecules i

L

Depth

LHAR

Microscopic lateral trenches86 

mi

Mass of molecules i

MEMS

Microelectromechanical sytems

MOF

Metal Organic Framework

n(t, z)

Precursor density

p

Perimeter

Pi

Partial pressure of reactant i

preaction

Reaction probability in a CVD-contributed process

P

Pressure

PD50%

Half-thickness-penetration-depth

PD80%

Penetration depth at which the film thickness is reduced to 80% of the original film thickness

PDMAT

Pentakis dimethylamino tantalum

Pr

Propyl

PVD

Physical vapor deposition

Py

Pyrrolyl

qji

Probability that the molecules coming from section j can reach section i

QCM

Quartz Crystal Microbalance

r

Recombination probability

rads

Adsorption rate

rdes

Desorption rate

ri

Radius of the molecule i

RBS

Rutherford Backscattering spectroscopy

σi,j

Cross-section between molecules i and j

s

Sticking probability

s0

Initial sticking coefficient

Si

Surface area of the discrete section i

SANS

Small Angle Neutron Scattering

SC

Step coverage

SCM

Shrinking Core Model299 

SE

Spectroscopic ellipsometry

SIMS

Secondary Ion Mass Spectrometry

θ

Fraction of covered sites

t

Pulse time

T

Temperature

TBTDET

Tris(diethylamido)(tert-butylimido)tantalum(V)

TBTEMAT

Tert-butylimino-tris-ethylmethylamino tantalum

TDMAT

Tetrakis(dimethylamido)titanium(IV)

TEM

Transmission electron microscopy

TEMAHf

Tetrakis(ethylmethylamido)hafnium(IV)

TMA

Trimethylaluminum

TTIP

Titanium(IV)isopropoxide

VOTP

Vanadium(V)oxytripropoxide

w

Width

XRF

X-ray fluorescence

XRR

X-ray reflectivity

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