Intermodulation spectroscopy as an alternative to pump-probe for the measurement of fast dynamics at the nanometer scale

Characterizing fast dynamical processes at the nanometer scale is key to understanding and optimizing the relation between structure and function in nanotechnology, where a particularly active field is the study of photo-induced charge dynamics in energy materials.^1,2 Optical pump-probe experiments³ routinely achieve femtosecond time resolution,⁴ with progress toward the attosecond regime,⁵ but they are typically diffraction-limited to hundreds of nanometers spatial resolution. Atomic force microscopy (AFM) is an ideal method for investigating material properties at the nanometer scale and considerable effort has been put into pushing the limits of time resolution in AFM.

Pushing the limit of measurement time in Kelvin-probe force microscopy has resulted in submillisecond resolution,² where multiple measurements at the same location are needed to follow the time evolution of the surface potential. In pursuit of dynamics faster than the measurement time, transients in the 10 μs range have been reconstructed by analyzing large datasets taken with high-speed data capture.⁶ Time resolution in the 10 ns range has been reached by monitoring the frequency shift⁷ or phase shift⁸ of an oscillating cantilever after an excitation pulse, averaging over several cycles. However, extracting recombination or relaxation time constants from the observed frequency shift is not trivial. Other approaches are closely related to the optical pump-probe technique, requiring repeated measurements at every tip position. The cantilever is used as a detector of an average potential signal, and a train of light pulses is used to excite the charge dynamics in the sample. The average potential is recorded as a function of the pulse-repetition frequency⁹ or the delay between two pulse trains,¹⁰ allowing for the calculation of the charge generation/recombination time constants.

In this manuscript, we demonstrate coherent multifrequency AFM methods to capture fast dynamics from several closely-spaced Fourier components of the force measured near the cantilever resonance where sensitivity is greatest. The use of the frequency domain multiplexes the pump-probe approach such that all delays are measured simultaneously or in the same time window.

Pump-probe spectroscopy explores fast dynamics with two pulses and a fixed delay. The response of the sample R is measured while a pump pulse excites some fast process, rapidly followed by a second probe pulse at fixed delay time t′. Repeating this measurement at different delay times gives R(t′), which contains information about the fast dynamics. The delay can be as short as the pulse width, and because it can be kept constant, long-time averaging over many identical events gives the desired signal-to-noise ratio (SNR). The events typically have some repetition frequency, but stability of this frequency is not a stringent requirement as each pump-probe event is considered statistically independent, and the assumption is that the sample relaxes to the same initial state in between events.

Here, we present frequency-domain alternatives to pump-probe which are also capable of resolving fast dynamics, in spite of the limited bandwidth inherent to a sensitive detector. The frequency domain approach exploits periodic signals or pulse trains that are carefully tuned such that they are coherent with one another, i.e., they have a fixed phase relation to a single reference oscillation. This allows for the lock-in measurement of the amplitude and phase of many intermodulation products generated by the nonlinear detection process. The fast time-domain response is then reconstructed through the Fourier analysis of the measured intermodulation spectrum.

Intermodulation spectral methods do not rely on the constancy of the delay. Rather, they exploit the stability of a reference oscillation, something that can be achieved with great precision.¹¹ Because the pump and probe signals are tuned, the time-domain delay between these signals changes in a regular manner over the entire long-time integration of the response signal, needed to determine the Fourier coefficients. At first sight, this frequency-domain approach may appear more complex, but it comes with an advantage over pump-probe: a tuned multifrequency lock-in measurement gives coherent signal averaging, where all frequencies in the measured spectrum are demodulated in parallel, during the same time window. This frequency-domain multiplexing reduces the measurement time needed to resolve the fast dynamics at the desired SNR.

In the following, we describe in detail the principles of intermodulation spectroscopy. We derive the theoretical limit of achievable time resolution for several different intermodulation methods, and we verify them through simulation and experiment. These different methods differ in their excitation schemes, but they all have the common feature that the material response is probed by a measurement of the force spectrum near the cantilever resonance, where force measurement sensitivity is at the thermal limit.

At frequencies near its first flexural eigenmode, the AFM cantilever is well approximated by a driven dampened harmonic oscillator described by the ordinary differential equation

where d(t) is the deflection of the cantilever from its equilibrium position and F is the total force acting on it. The calibration constants ω₀, Q, and k are the mode resonance frequency, quality factor, and stiffness, respectively. It is convenient to work with the Fourier transform of Eq. (1)

where $χ^(ω)$ is the linear response function of the cantilever,

In our notation, we use $f^(ω)$ to denote a complex-valued function of frequency given by the Fourier transform of the real-valued function of time f(t). In the following, we often drop the explicit frequency and time dependence.

When the AFM probe is lifted far away from the sample surface and the cantilever is in thermal equilibrium with the surrounding damping medium, a measurement of the power spectral density in a narrow band around the high-Q resonance reveals the thermal fluctuations of cantilever deflection, above the detector noise floor. From this measurement, we obtain the calibration constants ω₀, Q, and k, as well as the responsivity α (V/nm) of the deflection detector.^12–14 

Driving the cantilever with a force F_drive = F_D cos(ω_Dt + ψ_D) at a frequency ω_D ≈ ω₀ results in the “free” motion

Measuring $d^free$ provides knowledge of the drive force, eliminating the need for an independent calibration of the actuator. As the probe gets closer to the surface, additional linear forces act on the body of the cantilever which must be accounted for. These interactions are called background forces, and we compensate for their effect on the measured data by determining their linear response function $χ^BG$⁠, using a technique described in a previous publication.¹⁵ 

With the probe at the sample surface, we obtain the nonlinear “tip-sample” force from a measurement of the cantilever deflection $d^$⁠,

In electrostatic force microscopy (EFM), the dominant contribution to the tip-surface force is a nonlinear function of the potential difference V between the tip and the sample,

where the sample surface is at z = 0 so that z(t) = d(t) + h is the instantaneous tip-sample separation when the cantilever is deflected by d from the rest position h. The capacitance gradient ∂C/∂z is generally a nonlinear function of z.

Because the mechanical driving force F_drive is much larger than the perturbing electrostatic forces, the tip-sample separation to first-order in perturbation theory is

If the capacitance gradient is assumed to be analytic, it has the same time periodicity as z. We may therefore expand it in a Fourier series of ω_D

where {c_k} and {ϕ_k} are real numbers such that c_−k = c_k and ϕ_−k = −ϕ_k. The coefficient $ckeiϕk$ is the complex amplitude of the discrete Fourier transform (DFT) of ∂C/∂z at frequency kω_D. Note that while the first-order motion has only one component at frequency ω_D, the capacitance gradient has components at all harmonics of ω_D due to its nonlinearity.

We excite the sample with a periodic train of pulses (optical or electrical) with repetition frequency ω_E. In response to these pulses, a surface potential is generated and we may, similarly, expand the nonlinear term V² in a Fourier series of ω_E,

where {$v$_j} and {θ_j} are real numbers such that $v$_−j = $v$_j and θ_−j = −θ_j, and $vjeiθj=V2^(jωE)$⁠.

The product of Eqs. (8) and (9) introduces terms in Eq. (6) for $F^TS$ at frequencies that are integer linear combinations of ω_D and ω_E, the so-called intermodulation products (IMPs) or frequency-mixing products,

Depending on the choice of frequencies of the mechanical drive and electrical excitation, some IMPs arise close to the cantilever resonance where the high quality factor allows for a measurement of deflection (and therefore force) with the highest possible SNR. Below we analyze three cases where the optical or electrical excitation at ω_E is close to, below, and above resonance, while the mechanical drive at ω_D is kept at resonance.

The resonant scheme is analogous to that used in intermodulation AFM¹⁶ and has strong similarities to the traditional optical pump-probe method described above. The cantilever and the electrical response are both excited close to the resonance frequency at ω_D ≈ ω₀ and ω_E = ω_D + δ, with δ ≪ ω₀ [see Fig. 1(a)]. Some frequency components of the force in Eq. (10) arise close to the cantilever resonance and therefore produce a deflection measurable with good SNR. Numbering the IMPs by their position in the frequency domain with respect to ω_D (see Fig. 2), we give the frequency and the Fourier coefficients in Table I. These Fourier coefficients of the force around resonance depend on both the capacitance gradient (coefficients {c_k, ϕ_k}) and the electrical response (coefficients {$v$_j, θ_j}). However, we notice that the ratio and product of pairs depend only on the electrical response,

where we used the short-hand notation $F^n=F^TS(ωD+nδ)$⁠. Thus, we eliminate the dependence on the capacitance gradient from our analysis. No model for ∂C/∂z is required, and thereby we significantly decrease the number of free parameters. Furthermore, the ratio of force components is independent of the calibration constants k and α. We require only ω₀ and Q, which are directly measured with high accuracy, without relying on additional models and calibrations.^14,15

One problem with the resonant scheme is that electrical excitation so close to resonance can generate a relatively large Fourier coefficient $F^TS(ωE)$ that results in large response at ω_E, therefore violating the assumption of Eq. (7) and significantly decreasing the accuracy of the reconstruction method. To avoid this problem, we introduce two schemes where the electrical excitation is placed well below or above resonance.

This scheme is analogous to that used in intermodulation EFM,¹⁷ which mechanically drives the cantilever close to its resonance frequency at ω_D ≈ ω₀, while the electrical excitation is at a much lower frequency ω_E = δ ≪ ω_D [see Fig. 1(b)]. The frequency components of the force in Eq. (10) for k = 1 are those close to the cantilever resonance. With the numbering convention of Fig. 2, their Fourier coefficients are shown in Table II. Dividing all the measured force components by the force component at ω_D gives a complex coefficient depending on the electrical response only,

All measured IMPs near resonance are proportional to c₁, as opposed to c_1−n for the resonant scheme. For a smooth capacitance gradient, the coefficients c_k quickly drop in magnitude as k increases. Therefore, IMPs of the same order typically have higher magnitude (better SNR) when they are measured with the sub-resonant scheme, in comparison with the resonant scheme.

We excite the electrical response at frequency ω_E = 2ω_D + δ, close to the second harmonic of ω_D [see Fig. 1(c)]. With this scheme, $F^TS(ωE)$ and all its harmonics fall far from the cantilever resonance where the linear response function of Eq. (3) is very small. This trick is similar to that used by Dicke in one of the first implementations of a lock-in amplifier,¹⁸ where the modulation frequency at 30 Hz was locked to half the power-line frequency so that a spurious pickup at 60 Hz and its harmonics would not affect the measurement.

Only the down-converted IMPs arise close to the resonance, with Fourier amplitudes shown in Table III. Similar to the previous two schemes, we take the ratio and product of pairs of force components to eliminate the dependence on the capacitance gradient,

The amplitude of the IMPs close to resonance is proportional to c_1−2n. Therefore, as discussed above, the SNR of these IMPs measured in the super-resonant scheme is typically lower, in comparison with the resonant and sub-resonant schemes.

To estimate the achievable time resolution, we point out that intermodulation spectroscopy measures the change in the amplitude and phase of the cantilever deflection $d^$ from its free value $d^free$ [see Eq. (5)].

For the sub-resonant scheme [Fig. 3(b)], multiple cantilever oscillations (exactly n_D = ω_D/δ ≈ Q) probe every electrical excitation. The time resolution Δt of the reconstructed electrical response is therefore limited by the period T_D of a single cantilever-oscillation cycle,

For a typical 300 kHz cantilever, we get Δt_sub ≈ 3 μs. The time resolution is inversely proportional to the resonance frequency, and it can be improved by using shorter and stiffer AFM probes with higher resonance frequency.

For the resonant scheme [Fig. 3(a)], n_D cantilever oscillations are matched by n_E = n_D + 1 electrical excitations. This mismatch causes the delay between each oscillation and excitation to grow during the measurement window in multiples of a “base delay” T_D − T_E, where T_E is the repetition period of the electrical excitation. As for an optical pump-probe experiment, it is this base delay that sets the time resolution,

For a typical 300 kHz cantilever and a 2 ms measurement time, we get Δt_res ≈ 6 ns. The time resolution is inversely proportional to the square of ω₀, indicating that higher-frequency AFM probes would improve Δt much more than for the sub-resonant scheme. Moreover, we note how Eq. (15) depends on the experimental parameter δ; in principle, we can achieve an arbitrarily short time resolution by decreasing the frequency spacing δ and therefore increasing the measurement time T_M = 2π/δ. In practice, however, other experimental details limit the achievable time resolution, such as the frequency stability of the reference oscillation, the sharpness of the excitation pulse, and the slow drift of the cantilever linear response function (3) due to temperature fluctuations.

Similarly, for the super-resonant scheme [Fig. 3(c)] we find

The time resolution is improved by a factor of 2 with respect to the resonant case.

Our explanation of the achievable time resolution implicitly assumes that the entire cantilever deflection is measurable with good SNR. In the frequency domain, all the IMPs of $F^TS$ should be measurable above the noise level. In practice, however, this is not possible, and only a few IMPs are detected with appreciable SNR near the cantilever resonance. If we obtain coefficients $vneiθn$ up to order N, we can use the inverse DFT to obtain V²(t) (see Fig. 4), up to an offset and a scaling factor as all Fourier coefficients are in units of $v0eiθ0$⁠. The reconstructed signal has a time resolution of approximately

For the sub-resonant scheme, we typically have N = 10 and Δt = 2π/(Nδ) ≈ 200 μs. For the resonant scheme typically N = 5 and Δt ≈ 2π/(Nω₀) ≈ 0.7 μs.

Figure 4 demonstrates this limitation with a numerical simulation of the dynamics of the AFM cantilever in the sub-resonant scheme. The simulation includes realistic thermal and detector noise contributions, and the actual potential of the surface (the blue solid line) is a square wave with exponential rise and fall edges. The green solid line shows the response calculated with the inverse DFT of the coefficients $vneiθn$⁠, obtained from 10 IMPs in the simulated deflection spectrum. The curve captures the general shape of the response, but oscillations are clearly visible and the rise and fall edges are not as sharp as in the actual response.

In spite of this limitation, the time resolution can be significantly improved by introducing a physical model for the electrical response of the material. We need only a few Fourier coefficients of $V2^$ to accurately determine the parameters of the model, as shown by the orange dashed line in Fig. 4. By assuming a functional form for the response, we exploit correlations in the measured IMPs, pushing the time resolution toward the theoretical limit. The method described below is independent of the particular model for the charge dynamics and thus applicable to many different samples and materials.

We excite the sample with a periodic train of square pulses of length W and repetition period T_E = 2π/ω_E (the green line in Fig. 5). We model the electrical response of the sample as the blue and orange lines in Fig. 5, i.e., with an exponential rise and exponential fall characterized by the time constants τ_R and τ_F, respectively. These time constants model material properties such as charge generation, diffusion, and recombination. V_H and V_L are the equilibrium response with and without excitation, respectively, modeling the change in contact potential difference due to the excitation. In the case of fast dynamics, i.e., small τ_R and τ_F, the response reaches V_H and V_L within every pulse (the blue line in Fig. 5). However, for slow dynamics, the response only reaches V₁ < V_H and V₀ > V_L (the orange line in Fig. 5). The highest and lowest values reached are

The response V as a function of the “time-window coordinate” u = t mod T_E is therefore

It is possible to calculate analytically the DFT of V²(t) at ω = kω_E,

where ρ is a possible time delay in the electronics or measurement leads. $V2^$ is thus a function of frequency depending on the known experimental parameters T_E, W, and ρ and on four material parameters V_H, V_L, τ_R, and τ_F to be determined. We finally use a numerical least-square optimization routine to fit the material parameters to the measured Fourier coefficients of Eqs. (11), (12), or (13), obtaining V²(t) (the orange line in Fig. 4).

Having verified the theoretical analysis with numerical simulation as described above, we next turn to experimental verification with programmed voltage pulses. Figure 6 is a schematic representation of the measurement setup. A multifrequency lock-in amplifier¹⁹ (MLA) drives the cantilever at resonance and monitors the amplitude and phase of the deflection at some 40 frequencies around resonance, sending the data to a computer where the fit to the analytical model of Eq. (20) is performed to extract the material properties. In addition, the MLA has a separate arbitrary-waveform functionality, with which we apply a programmed voltage pulse shape to a smooth and conductive sample (highly oriented pyrolytic graphite, HOPG). Care is taken to transmit the pulses with 50 Ω-matched impedance, as close as possible to the sample surface. The electrical pulses affect the deflection of the AFM cantilever through the electrostatic tip-surface force.

In this control experiment, the electrical pulses are programmed with the shape shown in Fig. 5. The pulse parameters T_E, W, V_H − V_L, and τ_R are known and fixed during the experiment. The parameter V_L is a priori unknown as it corresponds to the contact potential difference between the HOPG and the AFM tip. Figure 7 shows the experimental data as well as the results of a numerical simulation. The value of τ_F obtained from the fitting routine is plotted versus the value programmed in the MLA. For the sub-resonant scheme, Fig. 7(b) shows that the achieved resolution, i.e., the lowest value the fit is able to reconstruct, is 4.6 ± 0.8 μs in experiments and 2.9 μs in simulations, in good agreement with 4.0 μs predicted by Eq. (14).

In the resonant scheme [Fig. 7(a)], experiments and simulations do not reach the predicted time resolution and deviate from the ideal reconstruction for small values of τ_F. The lowest value the fit is able to reconstruct is τ_F = 30 ± 6 ns in experiments and 14 ns in simulations, while the time resolution estimated from Eq. (15) is 0.8 ns. No noise contribution is added to the simulated data; therefore, we attribute this deviation to the violation of the assumption in Eq. (7), as discussed above.

In the super-resonant scheme, the time resolution predicted by Eq. (16) is 0.4 ns, a factor of 2 better than the resonance case. Noise-free simulations achieve 4 ns [Fig. 7(c)], but experimental results are limited to 75 ± 26 ns, worse than in the resonant case. If we include both detector and force noise in the simulations, we can reproduce the experimental data, suggesting that the experimental time resolution is indeed limited by the noise in the measurement. This suggestion is consistent with the lower SNR expected in super-resonant case, as discussed in Sec. III C. In the narrow band close to resonance used in our experiments, the limiting noise contribution is the thermal noise from the Brownian motion of the AFM cantilever. We therefore expect the method to perform better in vacuum conditions, where the increase in the quality factor and decrease in thermal noise allow for greater force sensitivity. Despite being still far from the potentially achievable limit, the demonstrated time resolution permits precise mapping of charge lifetimes in organic and inorganic photovoltaic materials, that have been show to span from microseconds to hundreds of milliseconds, depending on the material, illumination conditions, and sample preparation.^2,7,9

We also observe an upper limit to the time resolution. Even though the effect is not pronounced, the fitting of both the resonant [Fig. 7(a)] and the super-resonant [Fig. 7(c)] data starts to deviate from the programmed value of τ_F when it exceeds 2π/ω₀. Similarly, the fit gets worse in the sub-resonant case for values of τ_F above 2π/δ. In all three schemes, these upper limits correspond to τ_F exceeding the period of the programmed voltage pulse T_E when the applied potential is effectively constant. In these cases, a more traditional AFM technique like intermodulation electrostatic force microscopy¹⁷ (ImEFM) or kelvin probe force microscopy^2,20 (KPFM) that is able to measure the contact potential difference within a time 2π/δ ≈ 2 ms would yield more accurate results.

We theoretically derived, simulated, and demonstrated a novel AFM technique to measure the fast electrical dynamics of a material at the nanometer scale. With the use of frequency multiplexing, only one measurement is required to obtain the time-evolution of a process, as opposed to a pump-probe scheme where multiple measurements for different pump-probe delays are necessary. We achieved 30 ns time resolution with a commercially available AFM cantilever in ambient conditions, allowing for the mapping of nanosecond dynamics at standard tapping mode imaging speeds.

The authors acknowledge financial support from the Swedish Research Council (VR) and the Knut and Alice Wallenberg Foundation. D.B.H. is part owner of the company Intermodulation Products AB, which manufactures and sells the multifrequency lock-in amplifier used in this work.