Synaptic plasticity and metaplasticity of biological synapse realized in

Moreover, the KN memristor can emulate various biological synaptic plasticity ... essential property of biological synapses, is also realized in KN me...
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Synaptic Plasticity and Metaplasticity of Biological Synapse Realized in a KNbO3 Memristor for Application to Artificial Synapse Tae-Ho Lee,† Hyun-Gyu Hwang,‡ Jong-Un Woo,‡ Dae-Hyeon Kim,† Tae-Wook Kim,§ and Sahn Nahm*,†,‡ Department of Materials Science and Engineering and ‡Department of Nano Bio Information Technology, KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea § Applied Quantum Composites Research Center, KIST Jeonbuk Institute of Advanced Composite Materials, 92 Chudong-ro, Bongdong-eup, Wanju-gun, Jeollabuk-do 55324, Republic of Korea Downloaded via UNIV OF SUSSEX on July 25, 2018 at 07:24:27 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

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S Supporting Information *

ABSTRACT: Amorphous KNbO3 (KN) films were grown on a TiN/SiO2/ Si substrate to synthesize a KN memristor as a potential artificial synapse. The Pt/KN/TiN memristor exhibited typical and reliable bipolar switching behavior with multiple resistance levels. It also showed the transmission properties of a biological synapse, with a good conductance modulation linearity. Moreover, the KN memristor can emulate various biological synaptic plasticity characteristics including short-term plasticity, long-term plasticity, spike-rate dependent plasticity, paired-pulse facilitation, and posttetanic potentiation by controlling the number and rate of the potentiation spike. Spike-timing-dependent plasticity (STDP), which is an essential property of biological synapses, is also realized in the KN memristor. The synaptic plasticity of the KN memristor can be explained by oxygen vacancy movement and oxygen vacancy filaments. The metaplasticity of biological synapses was also implemented in the KN memristor, including the metaplasticity of long-term potentiation and depression, and of STDP. Therefore, the KN memristor could be used as an artificial synapse in neuromorphic computing systems. KEYWORDS: amorphous KNbO3 films, memristor, neuromorphic computing, artificial synapse, synaptic metaplasticity

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INTRODUCTION Neuromorphic computing systems have been intensively investigated as potential technology for the replacement of conventional digital computing systems because of energy efficiency, fault tolerance, and their capacity for adaptive learning.1,2 Neuromorphic computing is inspired by the biological brain, which can effectively perform complicated tasks because of its ability to handle computation and storage simultaneously.3 The human brain consists of a great number of neurons connected by synapses.4,5 Specifically, the synapse performs memory and learning through modulation of the connection strength between neurons.6 Therefore, the development of artificial synapses, which can mimic the biological synapse, is the first step to achieve an efficient neuromorphic computing system. There are various synaptic functions of the biological synapse that must be realized in the artificial synapse for application to neuromorphic computing. The synaptic plasticity, which is the capacity to modulate the connection strength of neurons (or synaptic weight), is an essential property of the biological synapse to be mimicked in the artificial synapse.7−9 Spike-ratedependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) are important properties belonging to the © XXXX American Chemical Society

class of synaptic plasticity, and these must be realized in artificial synapses.7−15 Biological synapses also have the properties of metaplasticity, which is the modulation of the synaptic plasticity that occurs by introducing a priming action before applying the main stimulus.16−18 A priming action does not change the synaptic plasticity; however, a larger modulation of synaptic plasticity is induced after the application of the main action. Therefore, metaplasticity changes the ability of the synapse to produce synaptic plasticity by applying the priming stimulus. Metaplasticity is a higher-order form of synaptic plasticity, and is needed to achieve the biorealistic artificial synapse, this property must also be realized in artificial synapses. Recently, metaplasticity has been reported in WO3 and (Na0.5K0.5)NbO3 (NKN) memristors, but further investigation of the various types of metaplasticity and their mechanisms are required.19,20 Previously, artificial synapses were fabricated based on complementary metal-oxide semiconductor (CMOS) analogue circuits.21,22 However, many capacitors and transistors are required to make this type of artificial synapse, and the high Received: March 20, 2018 Accepted: July 9, 2018 Published: July 9, 2018 A

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

Research Article

ACS Applied Materials & Interfaces

Figure 1. (a) I−V curves of the KN memristor were measured at various reset voltages. Resistances of the KN memristor in the (b) LRS and (c) HRSs measured at various temperatures.

was also realized in the KN memristor. The results of this work suggest that the KN memristor is a promising candidate for an artificial synapse applied to neuromorphic computing.

power and large volume with complicated circuits needed to operate the CMOS-based artificial synapse is an obvious disadvantage. Therefore, it is necessary to develop a new type of artificial synapse for application to neuromorphic computing systems. Recently, a two-terminal memristor, in which the resistance of the device can be continuously adjusted through controlling the applied voltage, has been proposed as an artificial synapse.23,24 The structure of a memristor with two electrodes and a functional film is very similar to a biological synapse consisting of two neurons with a connecting synapse. Moreover, the adjustable resistance of the memristor is very similar to the modulated synaptic weight of the biological synapse.8−15 Various memory devices have been suggested as artificial synapses such as ferroelectric random access memory, phase resistive random access memory (ReRAM), and resistive random access memory (ReRAM).25−32 In particular, ReRAM memristors are considered to be the leading candidates for artificial synapses in neuromorphic systems because the transmission characteristics and synaptic plasticity of biological synapses can be easily produced by ReRAM memristors.8−15,22,27 Furthermore, ReRAM memristors have good scalability to the nanometer regime, with low power consumption and good compatibility with silicon CMOS technology.22,33 Various materials have been used to fabricate the ReRAM memristor for application to artificial synapses: Ag2S, Cu2S, Ag/Si, InGaZnO, HfOx, WO3, TaOx, and TiO2/Ag composites.22,28,34−39 Lignin has also been used for the synthesis of artificial synapses.40 Recently, a (Na0.5K0.5)NbO3 (NKN) lead-free piezoelectric thin film has been used for the ReRAM memristor and the synaptic properties of the NKN memristor were reported.41 Moreover, an NKN nanogenerator was synthesized and it was used to supply power to the NKN artificial synapse.20 Hence, it is possible that a self-powered artificial synapse can be synthesized using a piezoelectric thin film for both the ReRAM memristor and the piezoelectric nanogenerator. KNbO3 (KN) is a promising lead-free piezoelectric material due to its large piezoelectric properties.42,43 Moreover, biocompatible amorphous KN thin films grown at a low temperature of 350 °C were used to synthesize both the ReRAM memristor and the piezoelectric nanogenerator.44 Moreover, the KN nanogenerator was used to supply the power to the KN memristor.44 However, the synaptic properties of the amorphous KN memristor have not been reported. Therefore, in this work, the KN memristors have been produced using amorphous KN thin films, and the various synaptic properties were emulated in them. Furthermore, the metaplasticity of a biological synapse

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EXPERIMENTAL PROCEDURES

Thirty-five nanometers thick KN films were grown on TiN/SiO2/Si (TiN−Si) substrates at 350 °C using an radio frequency magnetron sputtering method. The thicknesses of the TiN electrode and the SiO2 layer were 100 nm. The TiN−Si substrates were purchased from a private company (Dasom RMS, Anyang-Si, Gyeonggi-do, Korea). A KN ceramic target with a diameter of 2 in., which was synthesized using the conventional solid-state sintering method, was used for the deposition of the KN film.44 The KN films were deposited in a vacuum chamber under a mixture of Ar (80%) and O2 (20%) with a sputtering power of 80 W and a 10 mTorr working pressure. Pt was used as a top electrode (100 nm thickness), and was deposited on the KN films by direct current (DC) sputtering (Emitech K550) using a metal shadow mask (280 μm diameter) to form the metal−insulator−metal structure of the Pt/KN/TiN memristors, as shown in Figure S1c of Section 1 in the Supporting Information. The structure of the KN memristor was examined by X-ray diffraction (Rigaku D/max−RC, Tokyo, Japan), and high-resolution transmission electron microscopy (Tecnai G2 TF 30ST, FEI). The current−voltage (I−V) curves, DC-sweep endurance, and the retention properties of the KN memristor were measured by a source-meter (Keithley 2400). Biological synaptic characteristics were measured using a semiconductor characterization system (A Keithley 4200) and a pulse function arbitrary noise generator (Agilent 81150A). For the measurement of synaptic plasticity and metaplasticity of the KN memristor, the pulses of −1.2 V/10 μs and 1.2 V/10 μs were applied to the KN memristor as the potentiation spike and depression spike (Pand D-spikes), respectively, and all read pulse values were 0.1 V/10 μs. STDP, the key characteristics of synaptic plasticity, was realized in the KN memristor using pulse trains, which is a mixture of the pre- and post-spikes. In addition, to investigate metaplasticity, a priming P-spike (or priming D-spike) of −0.6 V/10 μs (or 0.6 V/10 μs) was applied to the KN memristor before the application of the main P-spike (or Dspike) of −1.2 V/100 μs (or 1.2 V/100 μs).

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RESULTS AND DISCUSSION The amorphous KN thin film was grown on the TiN−Si substrate at 350 °C for the fabrication of the KN memristor, as shown in Figure S1a−d of Section 1 in the Supporting Information. Figure 1a exhibits the I−V curves of the KN memristor that were obtained at various reset voltages (Vreset) ranging between 1.0 and 1.6 V. Five resistance states were observed in the I−V curves of the KN memristor: one low resistance state (LRS) and four high resistance states (HRSs). The four HRSs, H1, H2, H3, and H4 were obtained by applying the different Vreset values of 1.0, 1.2, 1.4, and 1.6 V, respectively. Moreover, these resistance levels show good electrical and B

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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ACS Applied Materials & Interfaces

Figure 2. I−V curves of the KN memristor measured under five consecutive (a) positive and (b) negative DC voltages. (c) Variations of voltage and current with respect to time and (d) variation of the conductance of the KN memristor with respect to the sweep cycle number. (e) Variation of the current of the KN memristor with the application of 500 P-spikes and 500 D-spikes.

I−V curves were measured when the five consecutive positive and negative DC voltage sweeps were applied to the device, as shown in Figure 2a,b, respectively. When five consecutive positive voltages (0−0.9 V) were applied, the Pt top electrode of the KN memristor, the current of the KN memristor decreased after each voltage sweep (see Figure 2a). When five consecutive negative voltages (0 to −0.9 V) were applied to the KN memristor, the magnitude of the negative current of the device increased, as shown in Figure 2b. To clearly show the changes of the current (or conductance), the voltage and current curves were plotted with respect to time, as shown in Figure 2c, and the conductance measured at the end of each sweep was also plotted with respect to the number of cycles (see Figure 2d). These results clearly illustrate that the application of repetitive DC bias can change the conductance of the KN memristor. Since the conductance of the KN memristor can be regarded as the synaptic weight, these gradual changes of the conductance emulated the transmission behavior observed in the biological synapse. In addition, when five consecutive positive and negative DC voltage sweeps were applied to the KN memristor, a current change in the milli-ampere range was observed (see Figure 2a− c). The current change in the NKN memristor, when similar five consecutive positive and negative DC voltage sweeps were applied, was in the micro-ampere range.20 Therefore, the number of oxygen vacancy filaments formed in the KN memristor during the DC voltage sweeps should be larger than that in the NKN memristor. The current was also measured after the application of the pulses; a series of 500 negative pulses (−1.2 V/10 μs) immediately followed by 500 positive pulses (1.2 V/10 μs) were applied to the KN memristor as the P-spike and D-spike, respectively. The current was measured at 0.1 V after each consecutive pulse. Figure 2e shows the variation of the current with the application of P-spikes and D-spikes. The current of the

thermal stability, as shown in Figure S2a−d of Section 2 in the Supporting Information. In addition, the electrical properties of the NKN film were degraded when the number of DC cycles exceeded 100.20 However, the KN memristor showed stable switching properties after 200 DC cycles, indicating that the KN film had better electrical reliability than the NKN film. More HRSs can be obtained by additional variation of the Vreset value, which changes the size of the oxygen vacancy filament (see Figure S3a−e of Section 3 in the Supporting Information), allowing continuous variation of the resistance level of the KN memristor. Therefore, this KN memristor can be used as an artificial synapse with various strengths of synaptic weight. The resistances of the LRS and four HRSs were measured at various temperatures, as shown in Figure 1b,c. The resistance of the LRS increased with the increase in the temperature, indicating that the LRS shows metallic conduction behavior. However, the resistances of the HRSs decreased with the increase in the temperature, and thus, the four HRSs behave as insulators. Moreover, the current in the LRS can be explained by the Ohmic conduction mechanism, and the conduction behavior of the four HRSs can be explained by the space-charge-limited conduction mechanism, as shown in Figure S4a−e of Section 4 in the Supporting Information. According to a previous study, the switching properties of the Pt/KN/TiN ReRAM memristor were explained by the formation and rupture of the oxygen vacancy filaments.44 Therefore, it can be suggested that the switching behavior of the Pt/KN/TiN memristor with the multilevel HRSs can also be explained by the formation and rupture of the oxygen filaments formed in the KN film. In the biological synapse, the synaptic weight, which is the strength of the connection between neurons, can be dynamically modulated and stored using consecutive spikes; this is known as the transmission behavior of the synapse.38,45 To investigate the synaptic transmission characteristics of the KN memristor, the C

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

Research Article

ACS Applied Materials & Interfaces KN memristor increased with the increase in the number of Pspikes and decreased with the increase in the number of Dspikes. The modulation of the current shown in Figure 2e indicates that potentiation and depression of the synaptic weight can be implemented in the KN memristor. According to previous works, various memristors showed a gap between the final stage of the potentiation process and the initial stage of the depression process; the presence of this gap was explained by the back-diffusion effect of oxygen vacancies (or oxygen ions).22,28,34−39 However, this gap was not observed in the KN memristor, indicating that the back-diffusion effect of oxygen vacancies is not significant in the KN memristor. Moreover, for application of the memristor to a neuromorphic computing system, linear conduction modulation is required when the same pulse was applied to the memristor.23 Previously, the ion diffusion limiting layer has been inserted between the film and the electrode to increase the linearity of the conduction modulation.46 For the KN memristor, the conductance increased abruptly at the short initial stage of the potentiation process, which is indicated by letter A in Figure 2e, but increased linearly after this initial stage, as indicated by II in Figure 2e. Moreover, the conductance decreased linearly in the depression process without a gap between the final stage of potentiation and the initial stage of the depression processes. Since the conductance of the KN memristor changes abruptly only in a very limited region, it can be suggested that the KN memristor exhibits a nearly linear conductance change with the application of uniform pulses. Therefore, it is considered that the KN memristor has good transmission properties for application to neuromorphic computing. In addition, the NKN memristor exhibited this gap, indicating that the KN memristor had better transmission properties.20 When P-spikes are applied to a memristor, oxygen vacancies are formed in the memristor and migrate to join the filaments and increase the current, resulting in potentiation. However, if the filament is weak, the oxygen vacancies will migrate back to their original positions after the removal of P-spikes due to the oxygen vacancy concentration gradient. Furthermore, when D-spikes are applied to the memristor immediately after the removal of the P-spikes, a large number of oxygen vacancies will suddenly be removed from the filaments due to the effects of the D-spike and the concentration gradient. Therefore, the current of the memristor will decrease drastically, resulting in the formation of the gap (or discontinuity of the current). However, if the filament is strong, the amount of the oxygen vacancies removed from the filament after the application of D-spike will not be large, and thus, the current will decrease continuously. Therefore, the filaments formed in the KN memristor were stronger than those formed in the NKN memristor. Synaptic plasticity is defined as the continuous modulation of the synaptic weight: the synapse performs learning and memory functions through the synaptic plasticity. The synaptic plasticity consists of the short-term plasticity (STP) and long-term plasticity (LTP), which correspond to short-term memory and long-term memory in psychology, respectively.46−49 For the human brain, short-term memory can be transformed into longterm memory by repeating the stimulation (a process of rehearsal), and this is one of the most important properties of human memory function.50 The transformation of STP into LTP was also investigated in the KN memristor. Figure 3a,i−iv shows the variation of the synaptic weight as a function of the retention time after the application of various numbers of Pspikes. The synaptic weight of the KN memristor decreased

Figure 3. (a) Variation of the synaptic weight of the KN memristor with respect to retention times after the applications of various numbers of Pspikes (i) = 1, (ii) = 20, (iii) = 40, and (iv) = 80. Variations of (b) the stable synaptic weight (LTP) values and (c) the value of τ with respect to the number of P-spikes.

rapidly after the removal of the P-spike and a small stable synaptic weight remained, as shown in Figure 3a,i, indicating that the LTP value is small after the application of one P-spike. As the number of P-spikes increased, the reduction of the synaptic weight after the removal of P-spikes was reduced and thus, the stable synaptic weight (or LTP value) increased, as shown in Figure 3a,i−iv. In particular, after the application of 80 P-spikes, a large stable synaptic weight was obtained, as shown in Figure 3a,iv, indicating that most of the STP was transformed into LTP. Therefore, it is considered that the transition of STP into LTP can be realized in the KN memristor by increasing the number of P-spikes. The memory-loss behavior in the human brain has been generally described by the exponential decay function, as shown in eq 1

i ty W (t ) = We + A expjjj− zzz (1) k τ{ where, W(t) and We are the synaptic weights (or memory level) at the time t and at steady state (at long time), respectively, A is a constant, and τ is the relaxation time constant, which is related to the forgetting rate; the small τ corresponds to a large forgetting rate.47−50 This equation was used to describe the relaxation behavior of the synaptic weight of the KN memristor; the relaxation curves of the synaptic weight of KN memristors were fitted using eq 1, indicated by the red curves in Figure 3a,i−iv. Moreover, the We and τ values were obtained from these calculations, as shown in Figure 3b,c. Small values of We (15% of the maximum synaptic weight) and τ (5.6 s) were obtained after the application of a P-spike, indicating that the KN memristor has a small LTP and a high forgetting rate. These values increased with the increase in the number of P-spikes, and a large We of 80% and a large τ of 45.2 s were obtained after 80 P-spikes. Therefore, it can be concluded that the increased LTP value and the decreased forgetting rate were obtained with the increased number of P-spikes due to the transition of STP to LTP. The mechanism for the transition of STP to LTP, which was induced by applying multiple P-spikes, can be explained by the movement of oxygen vacancies and the variation of the filament size, as shown in Figure S5a,b of Section 5 in the Supporting Information. Furthermore, since the variation of the current with respect to the retention time in the KN memristor could be described using eq 1, it can be suggested that the STP/LTP behavior of the biological synapse was observed in the KN memristor. D

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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current (or synaptic weight) with respect to the P-spike number measured at various P-spike intervals. When the P-spike interval is large (1 ms), the increase in the synaptic weight with the increase in the number of P-spikes is insignificant; a similar result was obtained for the P-spike with a 100 μs interval. However, the synaptic weight increased with the increase in the number of Pspikes for a shorter P-spike interval of 50 μs, and the largest synaptic weight was observed when P-spikes with a 10 μs interval were applied to the KN memristor. This effect can be more clearly observed in Figure 4b, in which the current difference between I1 and IN (ΔI) was plotted with respect to the pulse number measured at various P-spike rates, where I1 and IN are the currents measured after the 1st and Nth P-spikes, respectively. Figure 4a,b clearly shows that the more frequent spikes (high spike rate) induce larger changes in the conductance (or synaptic weight) of the KN memristor. Therefore, it is considered that the SRDP characteristic of the biological synapse is well implemented in the KN memristor. Variations of the (I2 − I1) and (I10 − I1) with respect to the Pspike interval were plotted in Figure 4c to further demonstrate the dependence of conductance (or synaptic weight) on the Pspike interval and number. (I2 − I1) and (I10 − I1) correspond to the paired-pulse facilitation (PPF) and post-tetanic potentiation (PTP) of a biological synapse, respectively. The PPF and PTP values are large at the small P-spike interval and decreased with the increase in the P-spike interval. Moreover, the (I10 − I1) value is much larger than the (I2 − I1) value at a small P-spike

The SRDP, which is the modulation of the synaptic weight with respect to the spike rate, is one of the important properties of synaptic plasticity.14,15 Figure 4a shows the variations of the

Figure 4. (a) Variations of the synaptic weight (or current) with respect to P-spike number and intervals. (b) Variation of the ΔI (=IN − I1) with respect to the P-spike number measured at different P-spike rates. (c) Variations of (I2 − I1) and (I10 − I1) with respect to the P-spike interval, which correspond to the paired-pulse facilitation (PPF) and posttetanic potentiation (PTP) of the biological synapse, respectively.

Figure 5. (a,i) Pre-spike and (a,ii) post-spike applied to the KN memristor, (b) a net spike applied to the KN memristor when Δt is 40 μs, and (c) variation in the ΔW value with respect to Δt. E

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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ACS Applied Materials & Interfaces

Figure 6. (a,i) P-spike applied to the KN memristor and (a,ii) the currents of the KN memristor before (state I) and after (state II) the application of the P-spike. (b,i) Priming P-spike and P-spike applied to the KN memristor. (b,ii) Currents of the KN memristor after the applications of priming Pspike (state III) and P-spike (IV). Schematic diagram of the KN film in (c,i) state I, (c,ii) state II, (d,i) state III, and (d,ii) state IV.

interval, but was almost the same when a P-spike with a large interval (1 ms) was applied to the KN memristor (see Figure 4c). These results clearly show that the conductance of the KN memristor is considerably influenced by the P-spike rate and number. The PPF of the KN memristor can be explained in terms of the migration of oxygen vacancies and the variation of the filament size. When a P-spike was applied to the KN memristor, oxygen vacancies were formed and migrated to join the filament, resulting in an increase in the current due to the increase in the filament size. After the removal of the P-spike, some of the oxygen vacancies diffused back to their original position due to the concentration gradient. When a second Pspike was applied to the KN memristor before the oxygen vacancies had diffused back to their original positions, new oxygen vacancies were incorporated into the filaments, resulting in a further increase in the filament size that induced an increased current compared with the first P-spike. Therefore, the migration of the oxygen vacancies and the variation of the filament size can explain the PPF. Moreover, when the interval of the P-spike was short, the current of the KN memristor increased continuously with the number of P-spikes due to the increase in the filament size; this process corresponds to the PTP. Therefore, the PTP of the KN memristor can also be explained by the movement of the oxygen vacancies and the variation of the filament size. In addition, the variation of the current of the KN memristor with the number and rate of the pulses was explained using the SRDP of the biological synapse. Therefore, it can be suggested that the SRDP of the biological synapse was realized in the KN memristor. The STDP, which is an essential characteristic of the synapse, is defined as the relative change in the synaptic weight (ΔW) due to the time difference between the pre- and the post-spikes (Δt);

the synapse is potentiated (ΔW > 0) when the pre-spike precedes the post-spike (Δt > 0) and the synapse is depressed (ΔW < 0) when the post-spike precedes the pre-spike (Δt < 0). In the KN memristor, the Pt electrode is a pre-neuron and the TiN electrode is a post-neuron. Therefore, the pre- and postspikes, which are shown in Figure 5a,i,ii, respectively, were applied to the Pt and TiN electrodes, respectively. The net spike applied to the KN memristor is determined by Δt and Figure 5b shows a net spike when Δt is 40 μs. In addition, Figure S6a−d of Section 6 in the Supporting Information show various net spikes applied to the KN memristor, which were determined by the Δt value. Figure 5c exhibits the variation of the ΔW value with respect to Δt, and the potentiation and the depression of synaptic weight were found at Δt > 0 and Δt < 0, respectively. Moreover, |ΔW| is large when |Δt| is small; it exhibits a large potentiation when Δt > 0 and a large depression when Δt < 0. Conversely, |ΔW| is small when |Δt| is large; it exhibits a small potentiation at Δt > 0 and a small depression at Δt < 0. Therefore, the KN memristor exhibited good STDP characteristics. STDP, which is observed in biological synapses, is generally expressed by the following equation l −|Δt | / τ+ o + ΔW0 + , Δt > 0 o o A+e ΔW = m o o o A −e−|Δt | / τ − + ΔW0 − , Δt < 0 n

(2)

where, A+ and A− are scaling factors, τ+ and τ− are the time constant, and ΔW0 is the same as ΔW when Δt is very large.8−13,28 The STDP curves of the KN memristor are well fitted with eq 2, as shown in Figure 5c. The A+/A− and τ+/τ− values were determined to be 119.7/−116.42 and 28.69/−85.65 μs, respectively. The change in the current depending on the F

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

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Figure 7. (a,i) D-spike applied to the KN memristor and (a,ii) the currents of the KN memristor before (state I) and after (state II) the application of D-spike. (b,i) Priming D-spike and D-spike applied to the KN memristor. (b,ii) Currents of the KN memristor after the application of priming D-spike (state III) and D-spike (state IV). Schematic diagram of the KN film of (c,i) state I, (c,ii) state II, (d,i) state III, and (d,ii) state IV.

state (state I), oxygen vacancy filaments were formed in the KN film (state II) (see Figure 6c,i,ii) resulting in the increase in the current in the KN film in state II (278 μA). When a priming Pspike was applied to the KN memristor, oxygen vacancies could be formed in the KN film due to the diffusion of oxygen ions into the TiN electrode; however, they did not contribute to the formation of filaments in the KN film (state III), as shown in Figure 6d,i because the current in state I is the same as that in sate III. When the P-spike was applied to the KN memristor after the priming P-spike, oxygen filaments were produced in the KN film as shown in Figure 6d,ii resulting in an increase in the current (state IV). Moreover, since the oxygen vacancies already existed in the KN film due to the application of the priming spike, the width of the oxygen vacancy filament in state IV (Figure 6d,ii) should be larger than that in state II (Figure 6c,ii). Therefore, the currents (or synaptic weight) in state IV is larger than that in state II. Moreover, it can be concluded that the priming P-spike increased the capacity of LTP through the formation of oxygen vacancies in the KN film. Metaplasticity of the long-term depression was also investigated in the KN memristor. The KN memristor in LRS showed a current of 282 μA (state I), as shown in Figure 7a,ii. When a D-spike of 1.2 V/100 μs was applied to the device (see Figure 7a,i), the current of the KN memristor decreased to 73 μA (state II), as shown in Figure 7a,ii. This process corresponds to the conventional long-term depression. However, when a Dspike of 1.2 V/100 μs was applied to the KN memristor after the application of the priming D-spike of 0.6 V/10 μs (see Figure 7b,i), the current of the KN memristor decreased to 56 μA (state IV), as shown in Figure 7b,ii. The current in state IV is smaller than that in state II, indicating that a larger reduction of the current occurred when a priming D-spike was applied to the memristor, even though the current of the memristor was not changed after the application of a priming D-spike (state III).

interval between the pulses applied to the Pt top and TiN bottom electrodes was interpreted using eq 2. Therefore, it can be concluded that the STDP characteristics of a biological synapse are easily realized in the KN memristor. The capacity of biological synapses for subsequent synaptic plasticity can be altered by introducing a priming spike before applying the main spike.16−18 This characteristic is defined as the metaplasticity (or plasticity of synaptic plasticity), and is a highorder form of the synaptic plasticity.16−18 The metaplasticity characteristic of the biological synapse was also investigated in the KN memristor. The KN memristor has a current of 22 μA at HRS (state I), as shown in Figure 6a,ii. When a P-spike (−1.2 V, 100 μs) was applied to the KN memristor (see Figure 6a,i), the current increased to 278 μA (state II), as shown in Figure 6a,ii. This process shows the conventional long-term potentiation with the increased synaptic weight. For the realization of metaplasticity characteristic, a priming P-spike (−0.6 V, 10 μs) was applied to the KN memristor before the application of the main P-spike, as shown in Figure 6b,i; however, the current of the device did not change (state III) (see Figure 6b,ii). However, when the main P-spike of −1.2 V/100 μs was applied to the KN memristor after the priming P-spike, the current of the KN memristor increased to 462 μA (state IV), as shown in Figure 6b,ii. The current of the KN memristor increased considerably with the application of a main P-spike after a priming P-spike, although the priming P-spike itself did not increase the current of the KN memristor. Furthermore, the current in state IV (462 μA) is larger than that in state II (278 μA). Therefore, it can be concluded that the priming P-spike increased the ability of the KN memristor to produce the large LTP. This result illustrates that the KN memristor shows metaplasticity of the LTP. Metaplasticity of the long-term potentiation can be explained by the behavior of the oxygen vacancies (or oxygen ions). When a P-spike was applied to the KN memristor in high resistance G

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ACS Applied Materials & Interfaces

Figure 8. Schematic diagram of the pulse trains consisting of a priming spike, a pre-spike, and a post-spike for (a,i) Δt > 0 and (a,ii) Δt < 0, which were applied to the KN memristor to realize the metaplasticity of STDP. Variation of ΔW values with respect to Δt when the (b) priming P-spike and Pspike and (c) priming D-spike and D-spike were applied to the KN memristor.

Therefore, it can also be concluded that the metaplasticity of the long-term depression was realized in the KN memristor. Metaplasticity of the long-term depression may also be explained by the behavior of the oxygen vacancies (or oxygen ions). When the D-spike was applied to the KN memristor in LRS (state I), which has the oxygen vacancy filament (see Figure 7c,i), the oxygen vacancies were diffused out from the filaments and joined with the oxygen ions, which were diffused out from the TiN electrode. Therefore, the current in the KN memristor (state II) decreased owing to the reduction in the length of the filament, as shown in Figure 7c,ii. When a priming D-spike was applied to the KN memristor, the oxygen vacancies were not diffused out from the filaments, and thus, the current in state III is the same as that in state I, as shown in Figure 7b,ii. When the D-spike was applied to the KN memristor after the priming Dspike, the oxygen vacancies diffused out from the filaments and reduced the current in the KN memristor. Since the current in state IV is smaller than that in state II, the size of the remaining filament in state IV could be smaller than that in state II, as shown in Figure 7d,ii. Moreover, it can be assumed that few oxygen ions were diffused out from the TiN electrode after the priming D-spike and existed at a near oxygen vacancy filament in the KN film without the recombination with the oxygen vacancies, as shown in Figure 7d,i. Therefore, a larger number of oxygen vacancies diffused out from the filaments when the D-

spike was applied to the KN memristor, the result of which is a smaller filament size in state IV, indicating that the metaplasticity of the long-term depression can be explained by the movement of the oxygen vacancies (or oxygen ions). In addition, when a priming D-spike was applied to the KN memristor, its current did not decrease, as shown in Figure 7b,ii. However, the current of the NKN memristor decreased slightly when a priming D-spike was applied because some of the oxygen vacancies diffused out of the filaments.20 This result also indicated that the oxygen vacancy filaments formed in the KN memristor were stronger than those formed in the NKN memristor. Metaplasticity of the STDP was also investigated for the KN memristor using a priming P-spike of −0.6 V/10 μs (or a priming D-spike of 0.6 V/10 μs). The pre- and post-spikes, which were used for the conventional STDP, were also used to realize the metaplasticity of STDP in the KN memristor. Figure 8a shows the pulse trains consisting of the priming spike, prespike, and post-spike, which were used for the realization of the metaplasticity in the KN memristor. Figure 8b shows the STDP curves including the metaplasticity effect due to the priming Pspike indicated by red circles and the conventional STDP curves indicated by black circles. The ΔW values at Δt > 0 increased due to the priming P-spike, whereas the ΔW values at Δt < 0 are the same as those of the conventional STDP. The STDP data H

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

Research Article

ACS Applied Materials & Interfaces including the metaplasticity effect were fitted using eq 2 and the A+/A− and τ+/τ− values were determined to be 124.66/−113.46 and 47.9/−84.74 μs, respectively. The A+ and τ+ values increased owing to the metaplasticity effect and the increase in the A+ and τ+ values eventually induced the increase in the ΔW values at Δt > 0, as shown in Figure 8b. However, the A− and τ− values were similar to those of the conventional STDP, and thus, the increase in the ΔW values at Δt < 0 was negligible after the application of a priming P-spike. Therefore, it can be concluded that the application of a priming P-spike increases the potentiation in STDP. The STDP curves illustrating the metaplasticity effect due to a priming D-spike and the conventional STDP curves are shown in Figure 8c, which are represented by red and black circles, respectively. The change of ΔW values at Δt > 0 can be negligible but the ΔW values increased at Δt < 0 when a priming D-spike was applied to the KN memristor. 2 was also used to evaluate the STDP curves with the metaplasticity effect; the A+/ A− and τ+/τ− values were determined to be 113.74/−222.7 and 29.73/−203.64 μs, respectively. The A− and τ− values negatively increased owing to the metaplasticity effect of a priming D-spike and the increase of ΔW values at Δt < 0 can be explained by the increases in the A− and τ− values, as shown in Figure 8c. On the other hand, the increase in the A+ and τ+ values are insignificant with the application of a priming D-spike. Therefore, it can be concluded that a priming D-spike increases the strength of depression in STDP, and that the metaplasticity of depression can also be realized in the KN memristor. Finally, since the effect of the priming-pulse on the final current of the KN memristor could be explained using the metaplasticity of the biological synapse, it can be concluded that the metaplasticity of the biological synapse was observed in the KN memristor.

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values at Δt < 0). Therefore, it can be concluded that the KN memristor is a good candidate for an artificial synapse.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.8b04550. Structural properties of KN films grown on TiN/SiO2/Si substrates; electrical reliability characteristics of the KN memristor; variation of the oxygen vacancy filaments with respect to the various V reset; current conduction mechanism for the KN memristor in LRS and HRSs; mechanism for the transition of STP into LTP; net spike and effective spike applied to the KN memristor for STDP (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Tae-Wook Kim: 0000-0003-2157-732X Sahn Nahm: 0000-0003-2192-5320 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4007189). We thank the KU-KIST graduate school program of Korea University.

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CONCLUSIONS

Amorphous KN films were grown on a TiN−Si substrate at 350 °C to fabricate a KN memristor. The Pt/KN/TiN memristor shows five resistance states by applying different Vreset values: one LRS and four HRSs. These resistance levels show good electrical reliability, proving that the KN memristor can be used as an artificial synapse with the various strengths of synaptic weight. The conductance of the KN memristor can be modulated by the application of repetitive DC bias, indicating that the transmission behavior observed in biological synapses can be realized in the KN memristor. Moreover, the KN memristor exhibits a nearly linear conductance change, without a gap between the final stage of the potentiation process and the initial stage of the depression process, upon the application of uniform pulses. Therefore, the KN memristor has transmission properties appropriate for its application to neuromorphic computing systems. Several synaptic plasticity characteristics including STP/LTP transition, SRDP, PPF, and PTP were implemented in the KN memristor through the control of the spike rate and number. STDP was also obtained from the KN memristor by controlling the temporal relation between the preand post-spikes. Metaplasticity of long-term potentiation (and long-term depression) was observed in the KN memristor by applying the priming P-spike (or priming D-spike) before the main P-spike (or D-spike), and this effect was explained by the variations of the width and length of the oxygen vacancy filaments. The metaplasticity effect of STDP was also realized in the KN memristor. Application of a priming P-spike (or a priming D-spike) increased the ΔW values at Δt > 0 (or the ΔW

REFERENCES

(1) Mead, C. Neuromorphic Electronic Systems. Proc. IEEE 1990, 78, 1629−1636. (2) Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B. D.; Adam, G. C.; Likharev, K. K.; Strukov, D. B. Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors. Nature 2015, 521, 61−64. (3) Yang, J.-L.; Sykora, P.; Wilson, D. M.; Mattson, M. P.; Bohr, V. A. The Excitatory Neurotransmitter Glutamate Stimulates DNA Repair to Increase Neuronal Resiliency. Mech. Ageing Dev. 2011, 132, 405−411. (4) Nestler, E. J. Molecular Basis of Long-term Plasticity Underlying Addiction. Nat. Rev. Neurosci. 2001, 2, 119−128. (5) Zucker, R. S.; Regehr, W. G. Short-term Synaptic Plasticity. Annu. Rev. Physiol. 2002, 64, 355−405. (6) Striedter, G. F. Neurobiology: A Functional Approach; Oxford University Press, 2015. (7) Bi, G.-q.; Poo, M.-M. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. J. Neurosci. 1998, 18, 10464− 10472. (8) Abbott, L. F.; Nelson, S. B. Synaptic Plasticity: Taming the Beast. Nat. Neurosci. 2000, 3, 1178−1183. (9) Watt, A. J.; Desai, N. S. Homeostatic Plasticity and STDP: Keeping a Neuron’s Cool in a Fluctuating World. Front. Synaptic Neurosci. 2010, 2, 240−255. (10) Song, S.; Miller, K. D.; Abbott, L. F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 2000, 3, 919−926. (11) Bi, G.-Q.; Poo, M.-M Synaptic Modification By Correlated Activity: Hebb’s Postulate Revisited. Annu. Rev. Neurosci. 2001, 24, 139−166.

I

DOI: 10.1021/acsami.8b04550 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX

Research Article

ACS Applied Materials & Interfaces (12) Caporale, N.; Dan, Y. Spike Timing−Dependent Plasticity: A Hebbian Learning Rule. Annu. Rev. Neurosci. 2008, 31, 25−46. (13) Dudek, S. M.; Bear, M. F. Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proc. Nati. Acad. Sci. U.S.A. 1992, 89, 4363−4367. (14) Brenowitz, S.; David, J.; Trussell, L. Enhancement of Synaptic Efficacy by Presynaptic GABAB Receptors. Neuron 1998, 20, 135−141. (15) Xu, C.; Zhao, M.-M.; Poo, M.-M.; Zhang, X.-H. GABAB receptor activation mediates frequency dependent plasticity of developing GABAergic synapses. Nat. Neurosci. 2008, 11, 1410−1418. (16) Abraham, W. C. Metaplasticity: Tuning Synapses and Networks for Plasticity. Nat. Rev. Neurosci. 2008, 9, 387. (17) Abraham, W. C.; Bear, M. F. Metaplasticity: The Plasticity of Synaptic Plasticity. Trends Neurosci. 1996, 19, 126−130. (18) Abraham, W. C.; Tate, W. P. Metaplasticity: A New Vista Across the Field of Synaptic Plasticity. Prog. Neurobiol. 1997, 52, 303−323. (19) Tan, Z.-H.; Yang, R.; Terabe, K.; Yin, X.-B.; Zhang, X.-D.; Guo, X. Synaptic Metaplasticity Realized in Oxide Memristive Devices. Adv. Mater. 2016, 28, 377−384. (20) Kim, B.-Y.; Hwang, H.-G.; Woo, J.-U.; Lee, W.-H.; Lee, T.-H.; Kang, C.-Y.; Nahm, S. Nanogenerator-Induced Synaptic Plasticity and Metaplasticity of Bio-Realistic Artificial Synapses. NPG Asia Mater. 2017, 9, No. e381. (21) Jo, S. H.; Lu, W. CMOS Compatible Nanoscale Nonvolatile Resistance Switching Memory. Nano Lett. 2008, 8, 392−397. (22) Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett. 2010, 10, 1297−1301. (23) Strukov, D. B.; Snider, G. S.; Stewart, D. R.; Williams, R. S. The Missing Memristor Found. Nature 2008, 453, 80−83. (24) Ohno, T.; Hasegawa, T.; Tsuruoka, T.; Terabe, K.; Gimzewski, J. K.; Aono, M. Short-Term Plasticity and Long-Term Potentiation Mimicked in Single Inorganic Synapses. Nat. Mater. 2011, 10, 591− 595. (25) Kim, D. J.; Lu, H.; Ryu, S.; Bark, C.-W.; Eom, C.-B.; Tsymbal, E. Y.; Gruverman, A. Ferroelectric Tunnel Memrister. Nano Lett. 2012, 12, 5697−5702. (26) Wright, C. D.; Liu, Y.; Kohary, K. I.; Aziz, M. M.; Hicken, R. J. Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials. Adv. Mater. 2011, 23, 3408−3413. (27) Yu, S.; Gao, B.; Fang, Z.; Yu, H. Y.; Kang, J.; Wong, H. S. P. A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation. Adv. Mater. 2013, 25, 1774−1779. (28) Wang, Z. Q.; Xu, H. Y.; Li, X. H.; Yu, H.; Liu, Y. C.; Zhu, X. J. Synaptic Learning and Memory Functions Achieved using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor. Adv. Funct. Mater. 2012, 22, 2759−2765. (29) Jeong, H. Y.; Lee, J. Y.; Choi, S.-Y. Interface-Engineered Amorphous TiO2-Based Resistive Memory Devices. Adv. Funct. Mater. 2010, 20, 3912−3917. (30) Bessonov, A. A.; Kirikova, M. N.; Petukhov, D. I.; Allen, M.; Pyhanen, T.; Bailey, J. A. Layered memristive and memcapacitive switches for printable electronics. Nat. Mater. 2015, 14, 199−204. (31) Wu, C.; Kim, T. W.; Choi, H. Y.; Strukov, D. B.; Yang, J. J. Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability. Nat. Commun. 2017, 8, No. 752. (32) You, B. K.; Kim, J. M.; Joe, D. J.; Yang, K.; Shin, Y.; Jung, Y. S.; Lee, K. J. Reliable Memristive Switching Memory Devices Enabled by Densely Packed Silver Nanocone Arrays as Electric-Field Concentrators. ACS Nano 2016, 10, 9478−9488. (33) Indiveri, G.; Chicca, E.; Douglas, R. A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses with Spike-Timing Dependent Plasticity. IEEE Trans. Neural Networks 2006, 17, 211−221. (34) Kundu, M.; Terabe, K.; Hasegawa, T.; Aono, M. Effect of sulfurization conditions and post-deposition annealing treatment on structural and electrical properties of silver sulfide films. J. Appl. Phys. 2006, 99, 103501−103509.

(35) Kundu, M.; Hasegawa, T.; Terabe, K.; Aono, M. Effect of sulfurization conditions on structural and electrical properties of copper sulfide films. J. Appl. Phys. 2008, 103, No. 073523. (36) Egorov, K. V.; Kirtaev, R. V.; Lebedinskii, Y. Y.; Markeev, A. M.; Matveyev, Y. A.; Orlov, O. M.; Zablotskiy, A. V.; Zenkevich, A. V. Complementary and Bipolar Regimes of Resistive Switching in TiN/ HfO2/TiN Stacks Grown by Atomic-Layer Deposition. Phys. Status Solidi A 2015, 212, 809−816. (37) Yang, R.; Terabe, K.; Liu, G. Q.; Tsuruoka, T.; Hasegawa, T.; Gimzewski, J. K.; Aono, M. On-Demand Nanodevice with Electrical and Neuromorphic Multifunction Realized by Local Ion Migration. ACS Nano 2012, 6, 9515−9521. (38) Wang, Z.; Yin, M.; Zhang, T.; Cai, Y.; Wang, Y.; Yang, Y.; Huang, R. Engineering Incremental Resistive Switching in TaOx based Memristors for Brain-inspired Computing. Nanoscale 2016, 8, 14015−14022. (39) Oh, K.; Jeon, W.; Lee, S.-S. One-Dimensional TiO2@Ag Nanoarchitectures with Interface-Mediated Implementation of Resistance-Switching Behavior in Polymer Nanocomposites. ACS Appl. Mater. Interfaces 2012, 4, 5727−5731. (40) Park, Y.; Lee, J.-S. Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials. ACS Nano 2017, 11, 8962−8969. (41) Kim, B.-Y.; Lee, W.-H.; Hwang, H.-G.; Kim, D.-H.; Kim, J.-H.; Lee, S.-H.; Nahm, S. Resistive Switching Memory Integrated with Nanogenerator for Self-Powered Bioimplantable Devices. Adv. Funct. Mater. 2016, 26, 5211−5221. (42) Lee, T.-H.; Kim, D.-H.; Kim, B.-Y.; Choi, H.-Y.; Oh, J.-H.; Kang, C.-Y.; Nahm, S. Structural and Electrical Properties of KNbO3 Thin Film Grown on a Pt/Ti/SiO2/Si Substrate Using the RF Magnetron Sputtering Method. Acta Mater. 2016, 112, 53−58. (43) Kim, D.-H.; Joung, M.-R.; Seo, I.-T.; Hur, J.; Kim, J.-H.; Kim, B.Y.; Lee, H.-J.; Nahm, S. Influence of Sintering Conditions on Piezoelectric Properties of KNbO3 Ceramics. J. Eur. Ceram. Soc. 2014, 34, 4193−4200. (44) Lee, T.-H.; Hwang, H.-G.; Jang, S.; Wang, G.; Han, S.; Kim, D.H.; Kang, C.-Y.; Nahm, S. Low-Temperature-Grown KNbO3 Thin Films and Their Application to Piezoelectric Nanogenerators and SelfPowered ReRAM Device. ACS Appl. Mater. Interfaces 2017, 9, 43220− 43229. (45) Jeong, Y.; Kim, S.; Lu, W. D. Utilizing Multiple State Variables to Improve the Dynamic Range of Analog Switching in a Memristor. Appl. Phys. Lett. 2015, 107, No. 173105. (46) Wang, Z.; Joshi, S.; Savel’ev, S. E.; Jiang, H.; Midya, R.; Lin, P.; Hu, M.; Ge, N.; Strachan, J. P.; Li, Z.; Wu, Q.; Barnell, M.; Li, G-L.; Xin, H. L.; Williams, R. S.; Xia, Q.; Yang, J. J. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 2017, 16, 101−108. (47) Nicoll, R. A. A Brief History of Long-Term Potentiation. Neuron 2017, 93, 281−291. (48) Bliss, T. V. P.; Collingridge, G. L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature 1993, 361, 31−39. (49) Rubin, D. C.; Wenzel, A. E. One Hundred Years of Forgetting: A Quantitative Description of Retention. Psychol. Rev. 1996, 103, 734− 760. (50) Chang, T.; Jo, S.-H.; Lu, W. Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor. ACS Nano 2011, 5, 7669−7676.

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