Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunnel-junction synapses

Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (SNN) based on spike-timing-dependent plasticity (STDP) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the STDP characteristics and spike types. Here, we report the STDP characteristics implemented in ferroelectric tunnel junctions (FTJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of FTJs into account, we propose equations for simulating the STDP curve parameters of a magnitude of the conductance change (ΔGmax) and a time window (τC) from the spike parameters of a peak amplitude (Vpeak) and time durations (tp and td) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. The power consumption experiments of the STDP revealed that the power consumption under the inactive-synapse condition (spike timing |Δt| > τC) was as large as 50–82% of that under the active-synapse condition (|Δt| < τC). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an SNN implemented by using FTJs as synapses.


Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunneljunction synapses p. Stoliar *, H. Yamada, Y. toyosaki & A. Sawa
Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (Snn) based on spike-timing-dependent plasticity (StDp) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the StDp characteristics and spike types. Here, we report the StDp characteristics implemented in ferroelectric tunnel junctions (ftJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of ftJs into account, we propose equations for simulating the StDp curve parameters of a magnitude of the conductance change (ΔG max ) and a time window (τ c ) from the spike parameters of a peak amplitude (V peak ) and time durations (t p and t d ) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. the power consumption experiments of the StDp revealed that the power consumption under the inactivesynapse condition (spike timing |Δt| > τ c ) was as large as 50-82% of that under the active-synapse condition (|Δt| < τ c ). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an Snn implemented by using ftJs as synapses.
In the internet of things era, energy-efficient computing systems are required to process the large amounts of data collected by sensors, such as in image and sound recognition. However, in conventional computing systems based on the von Neumann architecture, the frequent data transfer between spatially separated logic and memory units limits the processing speed, leading to energy inefficiency; this problem is known as the von Neumann bottleneck 1,2 . During the past decade, to overcome this problem, neural networks based on electronic devices have been intensively investigated [3][4][5] . In particular, spiking neural networks (SNNs) have been attracting increasing interest as a highly energy-efficient computing system. A typical SNN is composed of a tremendous set of computing nodes, which are regarded as artificial neurons 6 . Artificial neurons receive many inputs from other neurons in the form of electrical pulses or spikes. Artificial synapses weight input spikes and then summed up in a recipient neuron. After receiving the input spikes, the neuron performs a simple computation and generates output spikes 7 . This computation concept is taken from a biological system (Fig. 1a).
For SNNs, spike-timing-dependent plasticity (STDP) is one of the most studied learning mechanisms 8 . In the STDP mechanism, each artificial synapse receives pre-spikes and post-spikes from previous and post neurons, respectively (Fig. 1b). The weight of the synapse (synaptic weight) increases or decreases depending on the relative time difference (Δt) between pre-and post-spikes (Fig. 1c). Figure 1d presents a typical STDP curve in which a synaptic weight increases when a post neuron generates post-spikes immediately after receiving the pre-spikes (Δt > 0), meaning that the inputs are essential for the computation and are thus reinforced. On the other hand, when a post neuron receives pre-spikes after generating post-spikes (Δt < 0), the synaptic weight decreases. Resistive switching (RS) memories or memristors are often used to implement STDP artificial synapses in SNN circuits 9 because they have the functions needed for an artificial synapse, such as nonvolatility, reversibility, and National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan. *email: p.stoliar@aist.go.jp open resistance (conductance), and can be continuously adjusted (Fig. 1e). Therefore, for SNNs composed of RS memories, the synaptic weight corresponds to the conductance of RS memories.
Several types of spike shapes have been proposed to date to implement STDP learning with RS memories 10 . The different shapes of spikes lead to the different shapes of STDP curves. Simplistically, we characterized the shape of the STDP curve by two parameters, i.e., the amplitude of the conductivity modulation (ΔG max ) and the time window (τ C ), as described in Fig. 1d. These two values depend not only on the resistive switching characteristics of the RS memories but also on the shape of the spikes. Moreover, these parameters influence the computation performance of the SNNs. The spike-shape and timing dependence of STDP curves has been studied in RS memories 11,12 . However, there have been few systematic investigations on the spike-shape dependence of STDP characteristics in RS memories, which are needed to establish design guidelines for STDP artificial synapses.
Here, we report on the spike-shape dependence of STDP characteristics in RS memories, i.e., artificial synapses. In this study, we employed ferroelectric tunnel junctions (FTJs) with a BaTiO 3 (BTO) ferroelectric barrier as an RS memory 13 . FTJs are metal/ferroelectric insulator/metal junctions and show nonvolatile resistive switching in association with polarization reversal in the ferroelectric barrier, meaning that the resistive switching of FTJs is based on an electronic process 14,15 . Moreover, the resistance of FTJs can be changed continuously by tuning both the amplitude and the duration of the applied voltage pulses 16,17 . Because of this feature, STDP functionality has been demonstrated in FTJs [18][19][20] .
It should be noted that FTJs based on polarization reversal are expected to show much better switching stability than conventional RS memories based on conductive filaments, i.e., chemical reactions. Excellent switching stability is essential to ensure the reproducibility and reliability of results obtained from repetitive measurements, such as the STDP measurements. Thus, FTJs are considered suitable for our STDP experiment. To ensure the reproducibility of the results, we confirmed that our BTO-based FTJs showed no degradation of STDP characteristics after the completion of all measurements (see Supplementary Information for details). In this study, we demonstrated that the shape of the spikes influences the ΔG max and τ C values of the STDP curves in the BTO-based FTJs. The energy consumption by the update of synaptic weight dependent on the shape of spikes was also investigated. These results are discussed from the perspective of the relationship between the threshold voltage for resistive switching and the amplitude and the duration of superimposed spikes applied to the devices. Δt is spike timing. (e) Model of the artificial synapse implemented by using the FTJ and time evolutions of pre-(blue), post-(red) spikes, and superimposed spikes (green). V peak is the peak amplitude of the spike, V s is the peak amplitude of the superimposed spikes, and V th is the threshold voltage of resistive switching in the FTJ device.

Results
Resistive switching characteristics. The optical microscopy image and schematic of the BTO-based FTJs used in this study are shown in Fig. 2a. The junction area was 4 μm 2 , and the bottom and top metal electrodes were SrRuO 3 and Pt, respectively. The details of sample preparation are described in the Methods section. Figure 2b presents the pulsed current-voltage (I-V) characteristics measured by applying a sequence of voltage pulses V pulse (0 → 2.5 V → −2.5 V → 0, in 0.125 V increments, and a pulse duration of 3 ms). We observe an asymmetry (rectification) in the I-V characteristics resulting from an asymmetric potential distribution in the FTJ, as discussed in our previous report 13 . Note that in this work, we use the word "spike" to indicate complex voltage pulses utilized for the STDP measurements. On the other hand, we use the term "pulse" for a rectangular-shaped voltage pulse used for the standard electrical characterization of the FTJs.
To gain more insight into the memory properties of BTO-based FTJs, we also measured the hysteresis switching loop (HSL) 21 . Figure 2c shows the HSL of the BTO-based FTJ. Note that each data point in an HSL corresponds to a remnant conductance measured by applying a reading pulse (0.25 V) after a writing pulse (V pulse ). We also confirmed from repeated measurements of HSLs that the BTO-based FTJ showed practically nonvolatile resistive switching (see Supplementary Information). These results indicate that the BTO-based FTJ meets the requirements for an artificial synapse: it shows practically nonvolatile and reversible resistive switching and that the conductance (resistance) can be continuously adjusted by setting the amplitude of the writing pulse to an appropriate value. We also confirm the presence of threshold voltages for resistive switching at approximately ± 1 V. Such threshold behaviour of resistive switching was often observed for FTJs, and the threshold voltages coincide with the coercive voltages of ferroelectric barriers 16,17 . In our FTJs, we have confirmed the coincidence of threshold voltage and the coercive voltage obtained by a piezo-response force microscopy, indicating that the ferroelectric polarization-reversal is a dominant cause of resistive switching 13 . This threshold behaviour is useful for implementing STDP, as will be discussed later.
Note that the conductance change of HSL in our FTJ was much smaller than that of the FTJs composed of Nb-doped SrTiO 3 bottom-electrodes 22 . Although a large conductance change of FTJs is suitable for synaptic applications, we used prototypical Pt/BTO/SRO FTJs 23 in this study to investigate basic STDP properties of FTJs.

StDp characteristics.
To implement an STDP synapse using our FTJs, we may follow the standard procedure of STDP by applying a pre-spike V pre (t) to one terminal and a post-spike V pos (t) to the other (Fig. 1e) 9 . However, in this study, we adopted an equivalent procedure that is more convenient from the experimental point of view. One of the terminals was connected to ground, and the superimposed signal was applied to the other terminal. In this study, we investigated the STDP characteristics of our FTJs by applying five different types of superimposed signals ( Fig. 3k-o) consisting the corresponding pre-spikes ( Fig. 3a-e) and post-spikes ( Fig. 3f-j). Here, we classify the types of spikes as follows. For the spike shown in Fig. 3a,f, because it consists of two pulses whose amplitude decays exponentially with time, this type is classified as the EE type. The spike shown in Fig. 3b,g consists of a short-rectangular pulse and a pulse whose amplitude decays exponentially and is thus classified as the RE type. Since the spike shown in Fig. 3c,h consists of two triangle-shaped pulses, it is classified as the TT type. According to such definitions, the spike shown in Fig. 3d,i is classified as the RT type, and the spike shown in Fig. 3e,j is classified as the RR type. Figures 3p-t present typical STDP curves obtained corresponding to the five different types of superimposed signals ( Fig. 3k-o), respectively. In the STDP measurements, we first measured the device conductance G o and then applied a superimposed signal. Subsequently, the device conductance G a was remeasured. In the STDP curves, we plotted the change in the device conductance ΔG (=G o − G a ) as a function of Δt. As shown in Fig. 3pt, the FTJ showed STDP behaviour independently of different superimposed signals, i.e., pre-and post-spikes. However, the shape of STDP curves, which is characterized by the total modulation of the conductivity (ΔG max ) and the time window (τ C ) in this study, depended on the type of pre-and post-spikes, although the peak amplitude |V peak | of spikes was set to the same value for all measurements.
Note that for the measurements of Fig. 3p-t, the peak amplitude of the pre-and post-spikes was set to 1.25 V. When Δt was much larger than the spike width (≫4 ms), the spikes were not superimposed, and |V FTJ | remained within 1.25 V. As Δt approached 0, the spikes were superimposed, and |V FTJ | exceeded 1.25 V, which was larger than the threshold voltage for RS. As a result, the FTJ showed a non-zero ΔG at approximately Δt = 0, but ΔG = 0 at Δt = 0.
We also note that the STDP curves shown in Fig. 3 were obtained when the initial states of the device were set to an intermediate conductance state between the lowest and highest conductance states in the HSL. In this condition, the STDP curves were nearly symmetric. However, if the initial states of the device were set to close to the lowest or highest conductance states, decrease or increase in conductance became nearly zero, respectively, resulting in asymmetric STDP curves.
Dependence of spike types and parameters on the StDp curve. As mentioned above, the STDP characteristics of our FTJ depend on the type of spike. To gain more insight into the STDP characteristics, we evaluated the dependence of the spike types and parameters on the STDP curve. In this study, we chose three types of spikes, i.e., TT, RT, and RR, from the five types. This selection is because the STDP curves obtained using the EE-type and TT-type spikes were almost equivalent, and those obtained using the RE-type and RT-type spikes were almost equivalent, as shown in Fig. 3. We also systematically changed the peak amplitude (V peak ) and the time widths of two pulses constituting spikes. Here, we define two time-parameters: t p is the time width of the From the STDP curves, we extracted ΔG max and τ C (see Methods). Figure 4 shows the V peak and t d dependences of these two values for the TT-, RT-, and RR-type spikes with t p = 0.1 ms. Note that the results for t p = 0.2 ms and 0.3 ms are presented in the Supplementary Information. We did not perform the measurements for higher V peak values and longer t d values in order to avoid the degradation of the devices. In the experiments, the total energy dissipated by the device E max was limited to ~5 μJ. Figure 4a-e,i show the V peak dependence of τ C for the TT-, RT-, and RR-type spikes, respectively. For the TTand RT-type spikes, the τ c increased with V peak and t d . On the other hand, for the RR-type spike, the τ C increased with t d but was nearly independent of V peak at a fixed t d . These results can be understood from the relationship between the threshold voltage (V th ) for resistive switching and the peak amplitude of superimposed spikes (V s ), as shown in Fig. 5. In an ideal model, the τ C corresponds to the Δt at which ΔG starts to have a finite value for Δt < 0 or ΔG becomes zero for Δt > 0. At these Δt s , |V s | ≅ |V th | For the TT-type spike, the V s is given as s d peak for 0 < |Δt| ≤ t d . If |V peak | < 1/2|V th |, then τ C should be zero because |V s | is never larger than |V th |. This means that no STDP behaviour is observed. For 1/2|V th | ≤ |V peak | < |V th |, the Δt (=τ C ) at |V s | ≅ |V th | is given as C th peak d For |V th | ≤ |V peak |, τ C should be t d because |V s | is larger than |V th | independently of V peak . For the RT-type spike, the V s is given as for t p < |Δt| ≤ t d + t p . As shown in Fig. 5, however, the spike peak is not rectangular but sawtooth shaped. For the RS of the FTJs, not a peak amplitude of the voltage pulse but a time integral of applied voltage or displacement current is a main determinant of the magnitude of resistance change. This is because a ratio between the switched and unswitched ferroelectric domains in a ferroelectric barrier is proportional to the charges accumulated in the metal electrodes. For the sawtooth-shaped spike, therefore, the average amplitude (V s ave ) seems to be a good feature quantity of the spike peak since a time integral of the applied voltage corresponds to V s ave × t s , where t s is the time duration of the spike peak (Fig. 5b). The V s ave is given as If |V peak | < 1/2|V th |, then τ C should be zero. For 1/2|V th | ≤ |V peak | < |V th |, the τ C at |V s ave | ≅ |V th | is given as For |V th | ≤ |V peak |, τ C should be t d + t p independently of V peak . Equations (2) and (5) mean that the τ C increases with V peak and t d . These results seem to be qualitatively consistent with the results of Fig. 4a,e if 1/2|V th | ≤ |V peak | < |V th | for these experiments. On the other hand, for the RR-type spike, V s = 2V peak (=constant), when Δt < t d + t p and 1/2|V th | ≤ |V peak |. Therefore, the τ C is given as C d p independently of V peak . This finding seems to also be qualitatively consistent with the result of Fig. 4i, if 1/2|V th | ≤ |V peak | for this experiment. Figures 4c,g,k show the t d dependence of ΔG max for the TT-, RT-, and RR-type spikes, respectively. For the TTand RT-type spikes, ΔG max increased with t d and V peak , while ΔG max was nearly independent of t d at a fixed V peak for the RR-type spike. These results can be understood qualitatively from the t d dependence of V S or V s ave . Here, we assume that the ΔG max is given as a monotonically increasing function of (V S − V th ); ΔG max = f(V S − V th ). www.nature.com/scientificreports www.nature.com/scientificreports/ According to Eq. (1), V s increases with V peak and t d for the TT-type spike. As a result, ΔG max increases with V peak and t d . In the RT-type spike, V s is 2V peak for 0 < |Δt| ≤ t p , independently of t d . As discussed above, however, V s ave seems to be a good feature quantity of the spike peak for the RT-type spike, and the V s ave is given as s ave meaning that V s ave increased with V peak and t d . Therefore, ΔG max is expected to increase with V peak and t d for the RT-type spike. In contrast to the TT-and RT-type spikes, V s is 2V peak (constant) independently t d for the RR-type spike. Therefore, ΔG max for the RR-type spike is constant independently of t d at a fixed V peak .
Analysis of τ c and ΔG max . Here, we analyse the experimental results based on the above-mentioned equations. To quantitatively analyse the ΔG max , the function ΔG max = G[V spike (t)] must be determined, where G[V spike (t)] may be an integral function and V spike (t) is the time evolution of spike amplitude. However, it is difficult to determine G[V spike (t)]. We thus introduced empirical equations: . Because our FTJs showed the threshold-like HSL (Fig. 2c), we assume that the f(V S − V th ) or f(V s ave − V th ) are threshold functions as   for the TT-, RT-, and RR-type spikes, respectively. To analyse the results using these equations, the V th should be determined. In an actual FTJ, however, it is difficult to definitely determine the V th because the HSL did not show a clear threshold behaviour (Fig. 2c). Therefore, to obtain a V th value, we first fitted the results for the TT-type and the RT-type spikes by Eqs. (8) and (9), respectively. The solid lines in Fig. 4c,g indicate that the fitting results are in accordance with Eqs. (8) and (9), respectively and are in fairly good agreement with the experimental data. From the fitting results, we obtained a V th value of 0.63-0.73 V. Note that the prefactors of C were also obtained from the fitting results. Using the averaged V th value of 0.67 V, we then fitted the results for the RR-type spikes by Eq. (10), as indicated by the solid lines in Fig. 4k. Note that the values of fitting parameters (V th , Δt, and C) are described in the figure caption.
Next, τ C for the TT-, RT-and RR-type spikes are analysed using Eqs. (2), (5) and (6), respectively. Here, we used the averaged V th value obtained from the analysis of ΔG max . The solid lines in Fig. 4a,e,i are the calculated results of Eqs. (2), (5) and (6), respectively and are in fairly good agreement with the experimental data. These results confirm the validity of the equations for the analysis of the experimental results.
contour plots of τ c and ΔG max . We can simulate contour plots of τ C and ΔG max as a function of t d and V peak by using the above-mentioned equations with parameters obtained from analyses of the experimental results. Figures 4d,h,l are the contour plots of ΔG max and Fig. 4b,f,j are those of τ C for the TT-, RT-, and RR-type spikes, respectively. These contour plots can serve as a guide for controlling the STDP characteristics. This means that we can obtain a STDP curve with the desired τ C and ΔG max values by choosing the appropriate t d and V peak values, using these contour plots as a guideline. As shown in Fig. 6a, for instance, the STDP curves with τ C = 1 ms and ΔG max = 10 nS were obtained for the TT-, RT-, and RR-type spikes. The t d and V peak values used in these demonstrations were 1 ms and 0.5 V for the TT type, 1.2 ms and 0.55 V for the RT type, and 0.7 ms and 0.65 V for the RR type. (2019) 9:17740 | https://doi.org/10.1038/s41598-019-54215-w www.nature.com/scientificreports www.nature.com/scientificreports/ Measurements of the power consumption. One of the advantages of the SNN is low power consumption. As shown in Fig. 6, although the STDP characteristics are similar, the spike parameters used for obtaining the STDP curves are different among the spike types. This difference suggests that the power consumption depends on the spike types. To evaluate the spike type dependence of the power consumption, we measured the energy dissipated in the FTJ by conducting a sequence of 1000 pairs of pre-and post-spikes with randomly distributed Δt between −3 ms and 3 ms for the TT-, RT-, and RR-type spikes. The spike parameters were the same as in the experiments of Fig. 6a. We divided the data into two groups depending on whether |Δt| is larger than or smaller than τ C (=1 ms). Figure 6b presents the average energy per pair of pre-and post-spikes for each type of spike. The results show that the power consumption for the TT-type spike was smallest among the three spike types and that the power consumption for the RR-type spike was 3-5 times larger than that for the TT-and RT-type spikes. The energy dissipated in the FTJ is given as a time integral of I(t)V(t). If the FTJ shows a linear I-V curve because the dissipated energy is proportional to a time integral of V(t) 2 , then the power consumption for the RT-type spike is smallest among the three spike types. This discrepancy seems to be due to the nonlinearity of the I-V curve of the FTJ (Fig. 1b). In this case, the dissipated energy nonlinearly increases with voltage. Because the V peak value and the resultant V s value of the RT-type spike were larger than those of the TT-type spike, the dissipated energy for the RT-type spike might be larger than that for the TT-type spike in this study. For the RR-type spike, since rectangular pulses (i.e., constant voltage pulses) were applied to the FTJ, its power consumption was larger than that for the TT-and RT-type spikes.

Discussion
Here, we discuss which spike type is better for implementing an SNN with FTJs showing nonlinear I-V curves and HSLs, similar to our FTJs. From an engineering point of view, the RR-type spike is better because it can be generated by a simple electronic circuit and thus presumably uses less die area. As shown in Fig. 6b, however, its power consumption was 3-5 times larger than that for the TT-and RT-type spikes. On the other hand, the TT-type spike seems to be better at implementing an SNN in terms of power consumption. Nevertheless, its implementation is not adequate in real neuromorphic systems, unless a long delay time (t d ) with a triangular onset is utilized 24 . In our FTJ, the power consumption for the RT-type spike was slightly larger than that for the TT-type spike. As mentioned above, however, the power consumption depends on the nonlinearity of the I-V curve of the device. If the nonlinearity of the I-V curve can be optimized to reduce the power consumption, then the RT-type spike will be adequate to implement an SNN in terms of the power consumption and operating time.
It is well known that STDP is based on the Hebbian concept, namely, synaptic connections between neurons that fire together are reinforced 7 . However, synapses connecting uncorrelated neurons never become reinforced. Therefore, only a few active synapses are often reinforced by winner-takes-all algorithms 25 . In general, one neuron has thousands of synaptic connections, but only a few of them, i.e., active synapses, contribute to the computation. The active synapses that have relatively high conductivity transmit pre-and post-spikes with |Δt| < τ C . In www.nature.com/scientificreports www.nature.com/scientificreports/ an SNN circuit, however, inactive synapses also receive pre-and post-spikes with |Δt| > τ C and consume energy. Since the number of inactive synapses is larger than that of active synapses in a general SNN circuit, the total power consumption in inactive synapses is not negligible. As shown in Fig. 6b, the averaged power consumption in the inactive FTJs was 50-82% of that in active FTJs. If an SNN circuit is fabricated using our FTJs, then inactive synapses will be responsible for most of the power consumption. The power consumption of a FTJ under inactive-synapse condition depends on its resistance in the lower voltage range (<|V th |). Therefore, controlling the nonlinearity of the I-V curve, i.e., increasing the resistance in the lower voltage range (<|V th |), can reduce the power consumption in inactive synapses.

conclusions
We investigated the spike-type dependence of STDP characteristics for BaTiO 3 -based FTJs. To analyse the STDP curves characterized by an amplitude of the conductivity modulation (ΔG max ) and a time window (τ C ), we proposed empirical equations for three different spike types (triangle-triangle, rectangular-triangle, and rectangular-rectangular spikes) by taking into account the time evolution of the peak amplitude (V s ) of superimposed voltage spikes, the relationship between V s and the threshold voltage (V th ) for resistive switching, and the nonlinearity of the I-V curve of the FTJs. The proposed equations could reproduce the experimental results and provide a guideline for controlling the STDP characteristics. From the power consumption experiments of STDP, we found that the power consumption under the inactive-synapse condition (|Δt| > τ C ) is too large to ignore when an SNN is implemented by using our FTJs as synapses. Because the power consumption of a FTJ under inactive-synapse condition depends on its resistance in the lower voltage range (<|V th |), increase in the resistance at <|V th | is one of the solutions for this issue. The power consumption under the inactive-synapse condition may be an issue for an SNN circuit consisting of other FTJs, because the nonlinearity of I-V characteristics of other FTJs are similar to our FTJs. Therefore, this study will give a guideline for investigations of power consumption in neural network circuits consisting of FTJs.
Methods fabrication of ftJs. Oxide films were fabricated by pulsed-laser deposition using a KrF excimer laser. An SrRuO 3 (SRO) bottom-electrode layer was grown on a DyScO 3 substrate at a substrate temperature (T sub ) of 600 °C and an oxygen pressure (P O2 ) of 0.1 Pa. A BTO barrier layer was subsequently deposited at T sub ≈ 650 °C and P O2 ≈ 4.5 Pa. After the depositions, the heterostructures were cooled slowly in an atmosphere of oxygen (P O2 ≈ 10 Pa). The thicknesses of the SRO and BTO layers were 30 nm and 3.2 nm (8-unit cells), respectively. A 10-nm-thick Pt top-electrode layer was fabricated by electron-beam (EB) deposition. Subsequently, a cover-layer of Au (20 nm) was fabricated by EB deposition. The Au/Pt/BTO/SRO layered structure was patterned into 2 × 2 μm 2 junctions by conventional photolithography and Ar ion milling. For an insulation between the top and bottom electrodes, a SiO 2 layer (300 nm) was deposited by sputtering and patterned by a self-aligned lift-off method. Finally, an Au(400 nm)/ Ti(10 nm) wire metal for the top electrode was fabricated by EB deposition and the lift-off method. electrical characterizations of the ftJs. To characterize the FTJs, we developed a homemade source-measuring unit (SMU) and an associated acquisition software based on LabVIEW (National Instruments). This system can apply arbitrary voltage waveforms to devices and measure the current with sampling rates up to 625 kS/s. The devices were set in a Karl Suss PM8 Probe Station. For the I-V and HSL measurements, the writing voltage pulse lasted for 3 ms, and the device current was acquired with a noise level ~1 µA at a sampling rate of 10 kS/s (bandwidth = 40 kHz). We discarded the first ten measurements and averaged the remaining 20 measurements to obtain I pulse (Fig. 2b). For the HSL measurements, the reading voltage of 0.25 V with a time duration of 20 ms was applied after each writing pulse, and the current was acquired with a noise level <1 nA at a rate of 10 kS/s (bandwidth = 600 Hz). We discarded the first 50 measurements and averaged the remaining 150 measurements to obtain a conductance G in the HSL (Fig. 2c).
Measurement and parameter extraction from the StDp curves. We used the same setup for the I-V and HSL measurements to measure the STDP curves. We generated a long waveform (80000 samples in 8 s) containing 80 sets of pre-and post-spikes; each one occupies a time slot of 95 ms. After applying a pair of pre-and post-spikes, we applied a reading voltage of 0.25 V with a time duration of 5 ms to measure the conductance of the device. The first pair of pre-and post-spikes had Δt = 8 ms. The second one had Δt = −8 ms (i.e., post before pre). After the second one, pairs of pre-and post-spikes were applied by alternating positive and negative Δt and by decreasing |Δt| to 0 s in steps of 0.2 ms. After the measurements, a STDP curve was constructed by calculating the conductivity change (ΔG) between consecutive measurements. Note that experiments for shorter spike duration (<0.1 ms) were not conducted in this study because of limits in our experimental set-up (noise, speed).
To extract the amplitude of the conductivity modulation (ΔG max ), the time window (τ C ), and the difference of ΔG between Δt < 0 and Δt > 0 (ΔG 0 ), we multiplied the ΔG of STDP curve by −1 for Δt > 0 and obtained a modified STDP curve. The modified STDP curves were fitted with the formula ΔG = a/(1 + exp ((Δt − b)/c)). Then, ΔG max and τ C were extracted as ΔG max = a and τ C = −(c × ln(1/p − 1) + b), where ln(x) is the natural logarithm. The parameter p was chosen between 0.02-0.33 for each dataset to have a proper matching with Eqs.