Noise-assisted transport mechanism analysis and synaptic characteristics in ZrO_X/HfAlO_X-based memristor for neuromorphic systems

The explosive growth of data in recent years has resulted in numerous challenges for traditional computing systems that are based on the von Neumann architecture.¹ These systems rely on a centralized processing unit to execute instructions and access data stored in the memory. However, as the amount of data continues to increase, this approach has become a bottleneck as it requires a significant amount of time and energy to move data between the processing unit and memory.² This challenge has motivated the development of novel computing architectures that can overcome these limitations. In this context, neuromorphic computing has emerged as a promising alternative, inspired by the architecture and functionality of the human brain.³ Neuromorphic computing systems are designed to mimic the parallel processing and distributed memory capabilities of the human brain, enabling them to perform tasks such as pattern recognition and decision-making with remarkable efficiency. In recent years, numerous emerging memory technologies have been investigated for their potential in neuromorphic applications, including phase-change random-access memory (PRAM), ferroelectric random-access memory (FRAM), magnetic random-access memory (MRAM), and spin-transfer torque MRAM (STT-MRAM).^4–7 PRAM stores data by changing the phase of a chalcogenide material using heat, while FRAM uses a ferroelectric material to store data through polarization. MRAM and STT-MRAM rely on magneto-resistive effects to store data by changing the magnetic orientation of a thin-film element. Among the emerging memory technologies, resistive random-access memory (RRAM) is a promising candidate for neuromorphic applications owing to its properties of high speed, low power consumption, and high scalability. Because of its straightforward design and CMOS process compatibility, RRAM is also easily scalable and simple to integrate with existing electronic systems.⁸ RRAM is a non-volatile memory device that utilizes metal oxide layers to store information. Among the various metal oxide materials, TiO_X, TaO_X, NiO_X, ZnO, AlO_X, ZrO_X, and HfO_X have extensively been studied for their RRAM applications.^9–15 When metal oxides are used as insulators, resistive switching (RS) can occur owing to the presence of oxygen vacancies, which can form conducting filaments (CFs). These filaments can be broken by binding with oxygen ions, resulting in resistive switching. Therefore, the distribution of oxygen vacancies and ions significantly affects RS.¹⁶ 

Utilizing a bilayer structure in RRAM devices has been demonstrated to enhance RS properties because of regulated oxygen vacancy movements from the bilayer interface. This reduces the variability in resistivity, leading to more stable performance overall. Moreover, the bilayer configuration improves durability and reliability by enhancing information retention.¹⁷ Ye et al. revealed that the multilevel RS behaviors of HfO₂/TiO₂ bilayer structures exhibit excellent uniformity, high durability, and exceptional mechanical flexibility.¹⁸ Lee et al. found that ZrO_X/HfO_X bilayer-structured devices display great data retention at 85 °C, uniform distribution of the switching characteristics, strong switching endurance up to 10⁵ cycles, and lower reset currents.¹⁹ Previous research has shown that HfAlO_X-based RRAM devices demonstrate superior properties, such as large windows for resistance states, uniformity, and endurance, making them promising options for synaptic devices. Sokolov et al. reported that Ti/HfAlO_X/Pt RRAM devices achieve uniform and higher self-compliant RS behaviors.²⁰ Ryu et al. reported a Pt/HfAlO_X/TiN memristor device design with voltage-controlled synaptic behaviors and current sweeping mode for neuromorphic applications.²¹ Lin et al. reported that abundant oxygen vacancy exists in the ZrOx layer and that it has a hysteresis curve.²² Surazhevsky et al. reported that Au/ZrO₂/TiN devices have large on/off ratios and stable switching. In addition, it was found to be a suitable device for learning using spike-timing-dependent plasticity (STDP).²³ 

In the present study, a bilayer device was manufactured by atomic layer deposition (ALD) with 1.5 nm thick HfAlO_X at the ZrO_X/TiN interface. The bilayer device was operated with a lower compliance current (I_cc) than the single-layer device, allowing better control over the formation of CFs. This resulted in a lower conductance value for the bilayer device, which is beneficial in terms of power consumption. The RS mechanism was confirmed through the oxygen vacancy scheme. To clarify the transport mechanism in the RRAM device, we adopted a diagnosis tool involving 1/f noise measurement. The 1/f noise degrades the performance and stability of circuit systems and is one of the most critical limiting factors in circuit design. However, interestingly, 1/f noise analysis is a fast, repeatable, and nondestructive electrical characterization technique that analyzes the characteristics of devices such as working principle, transport mechanism, and trap analysis.^24–27 The compliance current and reset voltage were modulated to produce the multilevel RS characteristics. Furthermore, long-term potentiation and depression were attained both consistently and repeatedly. Accordingly, a modified National Institute of Standards and Technology (MNIST) database simulation was conducted using Python to test the pattern recognition accuracy.

The RRAM devices were fabricated as follows: First, a 100 nm-thick TiN layer was deposited on a SiO₂/Si substrate wafer via reactive sputtering in the presence of gaseous N₂ and Ar. In the Ti/ZrO_X/HfAlO_X/TiN device, a 1.5 nm HfAlO_X layer was deposited by ALD technology using tetrakis (ethylmethylamido) hafnium (TEMAHf) and trimethyl aluminum (TMA) as Hf and Al precursors, respectively, and ozone (O₃) was used as an oxidant. ZrO_X switching layers were subsequently deposited to a thickness of 20 nm using reactive sputtering at room temperature. Then, a 20 nm-thick Ti top electrode was deposited on the ZrO_X/TiN and ZrO_X/HfAlO_X/TiN substrates by DC magnetron sputtering. Finally, a Pt capping layer was deposited to prevent the oxidation of Ti when in contact with air.

The I–V and transient curves of the RRAM cells were obtained by measurements using a semiconductor parameter analyzer (Keithley 4200-SCS and 4225-PMU ultrafast current–voltage modules, Keithley Instruments, Cleveland, OH, USA). A bias was applied to the Ti top electrode, and the TiN bottom electrode was grounded. The 1/f noise characteristics were analyzed using a low-noise current amplifier (SR570) and a signal analyzer (35670A). The schematic diagram of the 1/f noise measurement system is shown in Fig. S1 (the supplementary material). The voltage applied to the top electrode was supplied by a semiconductor parameter analyzer (B1500A). The output current of the RRAM device was connected to the SR570 to convert the current fluctuations into voltage fluctuations. The 35670A then converted the dynamic signal from the SR570 into a power spectral density. The noise floor of our measurement system is ∼10⁻²⁴ A²/Hz, significantly lower than the device noise. This ensures that the noise power spectral density measured in this study is not affected by the measurement system noise floor.

The Ti/ZrO_X/HfAlO_X/TiN RRAM device stack is shown schematically in Fig. 1(a) and matches the transmission electron microscopy (TEM) image in Fig. 1(b). The amorphous ZrO_X layer is around 20 nm thick, and a clear interface is visible between the TiO_X and Ti/ZrO_X layers because the Ti top electrode is partially oxidized during deposition, creating a 7 nm-thick TiO_X layer. In addition, a 1.5 nm-thin HfAlO_X layer was applied by ALD at the TiN/ZrO_X interface. The chemical composition analysis of ZrO_X and HfAlO_X was meticulously performed using x-ray photoelectron spectroscopy (XPS) with a Nexsa G2 surface analysis system fromThermo Fisher Scientific. The detected peak positions were calibrated by performing Shirley background subtraction using the photoemission signal of the C 1s (284.6 eV). Subsequently, 5% Gaussian–Lorentzian functions were utilized to fit the XPS spectra. The high-resolution spectrum of the ZrO_X layer was presented in Fig. 1(c), and it was observed that the Zr 3d doublet could be divided into two groups of peaks corresponding to Zr in the metallic state (Zr⁰) at 178.7 eV and to a sub-oxide (Zr¹⁺) located at 179.8 eV binding energy.²⁸ The XPS analysis showed that doublets observed at higher binding energies (182.2/184.63 eV) were attributed to Zr³⁺ cations. Figure 1(e) shows that the Hf 4f spectrum of the HfAlO_X layer can be deconvoluted into two groups of peaks. Peak positions of Hf 4f_7/2 and Hf 4f_5/2 are at 17.4 and 18.4 eV, respectively. In addition, Fig. 1(f) shows that the Al 2p spectrum of the HfAlO_X layer can be deconvoluted into one peak. The peak position of Al 2p is at 74.0 eV. Figures 1(d) and 1(g) show the XPS plots of the O 1s peak spectra of the ZrO_X and HfAlO_X layers. Furthermore, the O 1s peak indicates the presence of different oxygen species in each layer of the memristor. The O 1s peak can be broken down into two components, designated O_L and O_V, respectively. The O_L and O_V peaks are related to lattice oxygen and non-lattice oxygen (oxygen vacancies). The concentration of O_L and O_V was calculated as the total area of both peaks divided by a single peak.²⁹ Based on the simulated spectra, the percentage of oxygen vacancies was calculated to be 16% in the top HfAlO_X layer, whereas it was 26% in the ZrO_X layer. The XPS analysis confirmed that the HfAlO_X layer had a lower concentration of oxygen vacancies. The XPS analysis confirmed that the HfAlO_X layer exhibited a lower concentration of oxygen vacancies.

The RS properties of ZrO_X-based RRAMs rely on the electroforming process, which triggers the movement of oxygen ions and the creation of oxygen vacancies.³⁰ Figures S2(a) and S2(b) (the supplementary material) illustrate the typical I–V curves for the forming processes of 30 randomly selected cells. The I_cc values of 10 and 1 mA were set for the single-layer and bilayer devices, respectively. It should be noted that the single-layer memory devices exhibited gradual increases from the high-resistance state (HRS) to the low-resistance state (LRS) and required much higher voltages than the bilayer memory devices. On the other hand, when the HfAlO_X layer was inserted, a low initial current was confirmed because the additional HfAlO_X layer acted as a series resistance. The bilayer device also transitioned from the HRS to the intermediate resistive state (IRS) at a lower voltage and then changed abruptly to the LRS. As illustrated in Fig. S2(c) (the supplementary material), a higher forming voltage distribution of the single-layer ZrO_X-based device is observed in the range from 12.0 to 16.0 V. In contrast, the bilayer ZrO_X-based device exhibits a low forming voltage distribution in the range of 6.0 to 13.0 V. From the results of the forming I–V curves, as described previously in Fig. S2 (the supplementary material), the bilayer structure of the ZrO_X-based device allows lower power consumption due to the lower forming voltage and I_cc, which are crucial for RRAM device applications. Figure 2(a) illustrates the typical I–V curves of the single-layer Ti/ZrO_X/TiN and bilayer Ti/ZrO_X/HfAlO_X/TiN devices. The voltage sweep direction is clockwise, and both devices exhibit bipolar RS characteristics. Fifty consecutive DC switching cycles were applied using negative set and positive reset voltages of −2.0 and 2.8 V without I_cc, respectively. In addition, Fig. S3 (the supplementary material) shows the results of 250 DC cycles and the conductance distributions of eight randomly selected memory cells during the cycling test of the double-layer RRAM device. It is very important for RRAM devices to operate reproducibly. To confirm that the bilayer device in this paper operates reproducibly, AC pulse endurance was additionally measured in addition to the existing DC 250 cycles. Based on the I–V curve of the device to be measured, the pulse scheme, as shown in Fig. S3(c) (the supplementary material), consisted of a set pulse (−1.0 V), a read pulse (0.2 V), a reset pulse (2.0 V), and a read pulse (0.2 V) in one cycle. As it proceeded for a total of 10³ times, the result is shown in the following figure: As shown in Fig. S3(d) (the supplementary material), the HRS was found to be worse than the initial state as it went on, but it was found to operate normally even after 10³ attempts. In the SET process, both single-layer and bilayer devices are abruptly SET below −1.2 V, demonstrating self-compliant characteristics. In the RESET process, both devices are abruptly RESET below −2.0 V. The initial current in the bilayer device is lower than that in the single-layer device owing to the presence of the HfAlO_X layer, which acts as a series resistance. Therefore, the bilayer device exhibits stronger RESET behavior than the single-layer device. In both the single-layer and bilayer devices, the self-compliance behaviors during the set transitions are attributed to the TiO_X layer acting as an oxygen-ion reservoir that prevents current overshoot.³¹ A DC endurance test and retention test were conducted to assess the device’s performance. The endurance tests were carried out for both the Ti/ZrO_X/TiN and Ti/ZrO_X/HfAlO_X/TiN devices at −0.2 V (V_read), as shown in Fig. 2(b). The HRS and LRS were maintained stably for 100 DC cycles, indicating excellent endurance characteristics for both devices. The on/off ratio for the Ti/ZrO_X/HfAlO_X/TiN device (>10²) was higher than that of the Ti/ZrO_X/TiN device (>10). Retention tests were also performed to confirm the performances of the single-layer and bilayer devices, as shown in Fig. 2(c); both devices exhibited no degradation over 10⁴ s. The bilayer device had a larger window than the single-layer device, similar to the endurance test results. The on/off ratio was significantly improved by the insertion of the HfAlO_X layer without any degradation in retention.

Figure 3 shows the schematics of the CFs for the single-layer Ti/ZrO_X/TiN and bilayer Ti/ZrO_X/HfAlO_X/TiN structures based on the forming process and I–V curve results. According to Gibbs’s free energy theory, the RS mechanism of the ZrO_X-based RRAM devices can be explained by the exchange of oxygen between the HfO₂, ZrO₂, and TiO₂ layers. The Gibbs free energy values for oxide formation in ZrO₂, HfO₂, and TiO₂ are ΔG^o = −1100.0 kJ/mol, ΔG^o = −1010.8 kJ/mol, and ΔG^o = −888.0 kJ/mol, respectively.³² The lower Gibbs free energy value for ZrO₂ formation implies that it has more oxygen vacancies (V_os) than HfO₂ and TiO₂.³³ Based on the XPS result as shown in Figs. 1(d) and 1(g), the ZrO_X layer contains a larger number of V_os than the TiO_X and HfAlO_X layers. In addition, according to a prior study, the size of the CF was larger in an oxygen-deficient layer than in an oxygen-rich layer. When a high current is applied, the narrowest portion of the filament (in this study, the TiO_X/ZrO_X and ZrO_X/HfAlO_X interface) heats up, allowing the migrated oxygen ions to reduce with the Joule heating effect and change LRS to HRS.^34,35 Given the above analysis, the SET process of the Ti/ZrO_X/TiN device is shown in Fig. 3(a). This process involves applying a negative voltage to the Ti top electrode, which causes oxygen ions to move toward the bottom electrode and undergo reduction reactions, turning them into oxygen. This reaction causes the oxygen vacancies to gain two electrons and become neutral, creating CFs and putting the device in its LRS. Applying a positive voltage to the Ti top electrode during the RESET process makes it easy for the migrated oxygen ions to recombine (oxidize) with the oxygen vacancies at the TiO_X layer. This leads to the rupture of the CFs at the top interface region, as shown in Fig. 3(b). The formation of CFs in the bilayer structure is similar to that in the single-layer structure. However, the bilayer structure with additional HfAlO_X shows a larger memory window than the single-layer structure, indicating that the HfAlO_X layer is also involved in the formation of CFs. As depicted in Fig. 3(c), when a negative voltage is applied to the Ti top electrode, the oxygen ions migrate toward the TiN bottom electrode, leaving behind oxygen vacancies. This leads to the formation of CFs from the bottom to the top electrodes, resulting in device switching from the HRS to the LRS. As depicted in Fig. 3(d), when the TE is applied to opposing voltages during the RESET procedure, the CFs in the TiO_X and HfAlO_X layers are ruptured, changing the device from HRS to LRS.

From a practical standpoint, the concept of multilevel memory is a fabulous approach to overcoming low-density memory limitations and creating connections between the memory and neuromorphic synapses.⁴⁹ In general, the multi-level cell (MLC) operation of the memristor can be obtained by adjusting the reset voltage, or I_cc. By adjusting the reset voltage, it is possible to control the gap between the electrode and the residual filament in the switching material. Otherwise, the lateral volume of the filament is controlled by changing the I_cc.⁵⁰ Due to the abrupt reset characteristics of the fabricated device, the MLC operation seems easier in the I_cc control mode. Figure 6(a) shows the I–V curves measured by adjusting I_cc from 500 µA to 6 mA during the SET process. Representative I–V curves for each I_cc are highlighted in color. As shown in Fig. 6(b), an MLC endurance test was conducted to confirm the possibility of multi-bit operation. Although there are overlapping parts among the seven cell states, a 2 bit operation (four-levels) was successfully achieved without any overlap. To examine the stability of each cell state, MLC retention characteristics were measured for up to 10⁴ s. As shown in Fig. 6(c), the cell states were maintained without any noticeable degradation.

For the implementation of neuromorphic systems, it is essential to effectively mimic the function of biological synapses using artificial synapses. Among the various devices being studied, memristors, which are two-terminal devices, are structurally the most similar to biological synapses, and it has been proven that various functions can be imitated.⁵¹ As shown in Fig. 7(a), in a biological neural system, presynaptic and postsynaptic neurons are connected through synapses. To transmit information from presynaptic neurons to postsynaptic neurons, presynaptic neurons first fire ions, which control the synaptic weight between neurons. When an action potential reaches a presynaptic neuron, the neurotransmitters are released into the synapse gap by diffusion of Ca²⁺ ions and are received by neuroreceptors on postsynaptic neurons.⁵² In this process of nervous system interaction, synapses are where neurons are functionally connected and are a key component of information transmission. This phenomenon can be modeled with a two-terminal memristor, and top/bottom electrodes, pulse input signals applied to the electrode, and conductance changes of the switching material according to pulse stimulation are counterparts of each element of the biological synapse, as shown in Fig. 7(b).⁵¹ In neuroscience, synaptic plasticity is attributed to the ability to change the strength and weight of connections between neurons, and this comes in different forms depending on the shape and timing of external stimuli.⁵³ Different types of plasticity are well described in Hebbian theory,^54,55 and representative synaptic behaviors include paired-pulse facilitation/depression, short- and long-term potentiation/depression, spike-number-dependent plasticity (SNDP), and spike-timing-dependent plasticity (STDP). Of these, STDP is considered the most central learning mechanism in Hebbian theory and plays an important role in information coding, learning, and memory.⁵⁶ 

STDP is a synaptic learning rule in the human brain that adjusts the connection strength between neurons based on whether spikes occur simultaneously or at regular time intervals.^57,58 Figure S5(a) (the supplementary material) shows the schematic of spike pulses. In STDP, the change in synaptic weight is the function of relative neuron spike timing ∆t (∆t = t_post − t_pre), where t_pre is the time when the presynaptic neuron spike arrives and t_post is the time when the postsynaptic neuron spike arrives. If the postsynaptic neuron spike arrives after the presynaptic neuron spike (∆t > 0), the synaptic weight increases, while if Δt is negative, i.e., the pre-spike follows the post-spike, then the synaptic weight decreases. The classical STDP curve originally described by Bi and Poo is depicted in Fig. 7(c).⁵⁹ In an artificial synapse based on a memristor, electrical pulse signals (spikes) can be applied to the memristor through the two electrodes and cause a modulation of the conductance of the memristor depending on the amplitude and relative timing of the pulses. That is, it is possible to realize the STDP function similar to the biological synapse systems. To provide an apparent demonstration of this phenomenon within our device, we executed a precisely calibrated application of a designed pulse stimulus to both the pre-synaptic and post-synaptic terminals of the Ti/ZrO_X/HfAlO_X/TiN device. Figure S5(b) (the supplementary material) shows the pulse schemes applied to our memristor. As shown in Fig. 7(d), when Δt > 0, the conductance (G) of the device gradually increases. On the other hand, when Δt < 0, the G value of the device gradually decreases. This tendency of synaptic weight change (∆W) measured in our memristor is in good agreement with that of biological synapses, which confirms the feasibility of our device for neuromorphic computing applications. STDP data points (five cycles) in Fig. 7(d) have a remarkable statistical scatter, which has also been observed in the biological synapses. Herein, the ∆W is defined by ∆W = (G_after − G_before)/G_min (after or before) × 100%, where G_after and G_before are conductances in the memristor before and after presynaptic and postsynaptic pulses are applied.⁶⁰ 

This study compares the resistance and synaptic properties of Ti/ZrO_X/TiN and Ti/ZrO_X/HfAlO_X/TiN RRAM devices. In the case of the Ti/ZrO_X/HfAlO_X/TiN device, the thin HfAlO_X layer acts as a series resistance that reduces the operating current. Therefore, it has advantages in terms of accuracy in distinguishing the on/off state and power consumption by having a lower operating current. We also clarify the transport mechanism in the Ti/ZrO_X/HfAlO_X/TiN device by LFN measurements. Furthermore, the MLC characteristics, potentiation, and depression were examined. An incremental pulse was used to adjust numerous synaptic weights to improve the precision of an artificial neural network. Finally, we evaluated the capability of the Ti/ZrO_X/HfAlO_X/TiN device to function as a neuromorphic device by examining its pattern recognition accuracy using MNIST results.

The supplementary material includes the distribution of electrical characteristics, the results of temperature measurements, and the pulse schemes for measurements of synaptic functions.

This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grants 2021M3F3A2A02037889 and RS-2023-00258527.

The authors have no conflicts to disclose.

J.H. and Y.C. contributed equally to this work.

Jungang Heo: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Writing – original draft (lead). Youngboo Cho: Data curation (lead); Formal analysis (lead); Investigation (lead); Writing – original draft (lead). Hyeonseung Ji: Investigation (supporting); Writing – original draft (supporting). Min-Hwi Kim: Conceptualization (supporting); Methodology (supporting); Validation (supporting). Jong-Ho Lee: Methodology (supporting); Supervision (supporting); Validation (supporting). Jung-Kyu Lee: Data curation (lead); Investigation (lead); Methodology (lead); Supervision (equal); Validation (equal); Visualization (lead); Writing – original draft (lead). Sungjun Kim: Conceptualization (lead); Funding acquisition (lead); Project administration (lead); Resources (lead); Supervision (lead); Validation (lead).

The data that support the findings of this study are available within the article and its supplementary material and from the corresponding authors upon reasonable request.