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Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
Nano Energy 66 (2019) 104095
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Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
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Chuan Qiana,b,1, Seyong Ohc,1, Yongsuk Choib, Jeong-Hoon Kimc, Jia Sund, Han Huangd, Junliang Yangd, Yongli Gaod,e, Jin-Hong Parkb,c,∗∗, Jeong Ho Choa,∗ a
Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 120-749, Republic of Korea SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea c Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea d Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, PR China e Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Solar-stimulated optoelectronic synapse Neuromorphic computing Organic heterojunction Band engineering Pattern recognition
We report an artificial optoelectronic synapse based on a copper-phthalocyanine (CuPc) and para-sexiphenyl (p6P) heterojunction structure. This device features stable conductance states and their linear distribution in longterm potentiation (LTP) characteristic curve formed by continuous input light pulses. These superior synaptic characteristics originate from the fact that the number of photo-holes moving into the CuPc channel and photoelectrons being trapped at the p-6P/dielectric interface is constant at every light pulse. A single-layer neural network is theoretically formed with these optoelectronic synaptic devices and its feasibility is studied in terms of training/recognition tasks of the Modified National Institute of Standards and Technology digit image patterns. Owing to the excellent LTP characteristic and through the use of a unidirectional update method, its maximum recognition rate is as high as 78% despite the use of a single-layer network. This study is expected to provide a foundation for future studies on optoelectronic synaptic devices toward the implementation of complex artificial neural networks.
1. Introduction In the field of artificial intelligence, AlphaGo, seeking hegemony in the game of Go, has attracted considerable interest [1]. Recently, through self-play, AlphaZero defeated a world champion program in the game of chess [2]. The learning algorithm for an artificial neural network (ANN) based on von Neumann computing has evolved rapidly and is a leading technological trend [3,4]. However, these conventional digital computing systems are configured with separate data-computing (CPU) and data-storing (memory) units, where the data processing for learning complex ANNs is limited by the data exchange rate [5,6]. To overcome this bottleneck, a bio-inspired electronic device, called “synaptic device,” which can process and memorize information simultaneously, has recently attracted significant attention. So far, many types of devices have been proposed as artificial synaptic devices, such as memristors [7,8], phase change memory [6,9], ferroelectric transistors [10,11], electrochemical transistors [12–16], and field-effect transistors
[17,18]. In a biological synapse, synaptic weight is persistently strengthened or weakened by signal transmission between presynaptic and postsynaptic neurons, resulting in long-term potentiation (LTP) or long-term depression (LTD), respectively [19]. For successful implementation of these synaptic dynamics, a linear weight update with sufficient conductance states is essential [20,21]. However, in many prior studies on electronic synaptic devices, synaptic response induced by continuous voltage pulses was different in each pulse and each cycle, which has hindered the linear and uniform modulation of the conductance states. As a nonlinear weight update is one of the major obstacles in implementing complex ANNs composed of synaptic devices, the studies on synaptic devices with highly linear LTP/LTD characteristics must be conducted. In contrast to electrical signals, light uses a wireless medium without transmission loss, and it is even used to control cells with lightsensitive protein in living tissues of nematodes, fruit flies, mice, etc [22–24]. Moreover, processing of visual detail as sight in a visual
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Corresponding author. Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 120-749, Republic of Korea. Corresponding author. SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea. E-mail addresses: [email protected] (J.-H. Park), [email protected] (J.H. Cho). 1 The authors contribute equally to this paper. ∗∗
https://doi.org/10.1016/j.nanoen.2019.104095 Received 9 July 2019; Received in revised form 2 September 2019; Accepted 3 September 2019 Available online 06 September 2019 2211-2855/ © 2019 Elsevier Ltd. All rights reserved.
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system-level ANN. The numerical information required for aligning the energy bands of CuPc, p-6P, SiO2, and Si is shown in Fig. S3 [33–35]. Under equilibrium state (VG = 0 V), some electrons are trapped in the p6P layer near the interface with the dielectric layer and hole carriers appear to be slightly accumulated near the interface with the p-6P layer. When three types of light pulses (wavelengths = 655, 450, and 365 nm) were applied to the CuPc/p-6P optoelectronic synapse, we could observe different postsynaptic current (PSC) responses, as shown in Fig. 1d. The light pulse signals were absorbed in the heterojunction channel and were transformed to electrical signals via an electron–hole generation process. When a 655 nm light pulse of power 1.0 mW was applied, the PSC increased up to 4.64 nA; however, it returned to its initial current level after approximately 2 s. This is because the photogenerated carriers instantly increased the channel conductance and the carrier injection rate from the presynaptic terminal, and they rapidly recombined with each other. Under the illumination of a 655 nm light pulse, electron and hole carriers are readily generated because the photon energy (Eph = 1.89 eV) is larger than the bandgap of CuPc (1.65 eV). These carriers quickly recombine because there are numerous electrons and holes in the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) bands, respectively. Under the illumination of a 450 nm light pulse (Eph = 2.76 eV), a similar response was observed, but its peak PSC value (4.52 nA) was smaller than that in the previous case. This may be because both CuPc and p-6P present relatively low light absorption in the wavelength region near 450 nm. When a 365-nm light pulse was applied, a larger PSC (its peak value was 4.67 nA) was observed despite lower power (P = 0.1 mW), compared with the previous two cases (655- and 450-nm lights with P = 1.0 mW). Especially, the PSC was reduced slightly and maintained over 10 s (approximately 4.64 nA). The smaller decay of the PSC is because the carriers generated by the 365nm light pulse have a relatively longer lifetime (lower recombination rate), compared to the other cases. As shown in Fig. 1e, under VG = 0 V, the holes generated in the p-6P layer are predicted to move to the CuPc channel region and the remaining electrons are probably trapped at the p-6P/dielectric interface. The absence of holes in the p-6P region and the additional supply of electrons into the CuPc region cause an asymmetric carrier distribution, thereby reducing the carrier recombination rate. Of course, carrier traps such as imperfectly terminated bonds and grain boundaries will increase the recombination rate of carriers. However, the amount of the traps is predicted to be not much large because the PSC was stably maintained for a long time after the light pulse disappeared (Fig. 1d). In addition, the time-dependent change in the average PSC on the CuPc surface of the p-6P/CuPc structure was observed using in situ CSAFM after exposure to the 365 nm light pulse. As shown in Fig. 1f, the PSC increased from 42.3 (dark brown) to 81.9 pA (light yellow) under the light illumination. When the light was turned off, the PSC was reduced to 65.2 pA after 10 min, to 53.3 pA after 20 min, and then to 49.6 pA after 30 min. Approximately 61% of the peak current value was retained even after 30 min because the holes transferred from the p-6P layer cause an asymmetric carrier distribution in the CuPc and consequently stay in the HOMO band of the CuPc for a long time with fewer recombinations. However, for the device only composed of a CuPc film, the PSC returned quickly to its initial current level (Figs. S5 and S6). This is presumably because the electrons and holes generated in the CuPc film recombined in a short time owing to their symmetric distribution. As this CuPc/p-6P synaptic device was based on a phototransistor with charge-storing characteristic, we could control its synaptic weight (conductivity) by using both light and gate voltage simultaneously. When a light pulse (365 nm, 1.0 mW, 0.5 s) was applied to this device under three different gate voltages (+5, 0, and −5 V), we observed different synaptic current responses, as shown in Figs. 2a and S7. Here, ΔPSC was defined as the difference between postsynaptic currents before and after the light illumination. When VG of +5 V was applied,
system is a unique and important part in a biological neural network, where the retina is a light-sensitive layer transforming light signals to electrical pulses, similar to a photodetector. Thus, it is meaningful to implement a synaptic device enabling the transformation of light signals to electrical information. Until now, many studies on artificial optoelectronic synapses have been reported [18,25–31]. Lee's group mimicked a sensorimotor nervous system [27], and Wang's group fabricated two terminal optoelectronic synapses based on organolead-halide-perovskite [29]. In addition, Prof. Su-Ting Han's group emulated the basic optosynaptic behaviors in a type-II heterojunction device and measured the dynamic states of carrier redistribution using a Kelvin probe force microscope [18]. However, these studies mainly focused on the basic synaptic dynamics, and few studies showed the application to ANNs. Therefore, the feasibility of optoelectronic synapses toward complex neural networks must be studied further. Here, we demonstrate an artificial optoelectronic synapse achieved through band engineering of an organic heterojunction and then confirm its feasibility for ANNs in terms of the training/recognition tasks of the Modified National Institute of Standards and Technology (MNIST) digit image patterns. The heterojunction consists of copper-phthalocyanine (CuPc) and para-sexiphenyl (p-6P) layers [32]. Under various 365 nm UV light pulse conditions, solid synaptic responses (LTP) were observed using in situ current-sensing atomic force microscopy (CSAFM) and electrical measurements. In particular, conductance states varied by the continuous light pulses were stable and linearly potentiated, accordingly enhancing the recognition rates for the MNIST digit patterns with the assistance of a unidirectional update method. This perfectly linear LTP characteristic originates from the fact that the numbers of holes moving into the CuPc channel and electrons being trapped at the p-6P/dielectric interface are constant at every light pulse. 2. Results and discussion Biological synapses containing light-sensitive proteins are known to receive and transmit light signals, in addition to electric signals [22–24]. To mimic such optosynaptic dynamics, we fabricated an artificial optoelectronic synaptic device by using a CuPc/p-6P organic heterojunction. Fig. 1a shows the schematic diagram of the synaptic device, which uses both optical and electrical signals to modulate its channel conductivity. Here, the source and drain electrodes are defined as pre- and post-synaptic terminals, respectively. As the channel conductivity is directly related to the synaptic weight of a biological synapse, the synaptic weight is strengthened by a light pulse illuminated on top of the device and it is weakened by a back-gate voltage pulse. In this optoelectronic synaptic device, the active channel consisting of the CuPc/p-6P heterojunction is the most important part. This is because the CuPc/p-6P thin films are very sensitive to light, thus, providing a foundation for finely controlling the conductivity with a low-energy light pulse. In the heterojunction channel, the p-6P layer was grown in the form of discontinuous islands based on the Stranski–Krastanov growth mechanism; however, it improved the morphology of the overlying CuPc thin film with ordered arrangement and also improved its electrical conductivity (Fig. 1b and Fig. S1). Particularly, owing to the discontinuous crystal formation and large energy bandgap of the p6P layer, an effective carrier channel is expected to be formed in the CuPc layer. Consequently, this CuPc/p-6P heterojunction channel with a properly aligned band structure (Fig. 1c) not only considerably improved the photoresponse of the synaptic device (Fig. S1), but also enabled the implementation of the most essential feature for nonvolatile LTP/LTD characteristics. In order to confirm the uniformity of our organic synaptic devices fabricated onto 2 × 2 cm2, the device-todevice variation was investigated. As shown in Fig. S2, the proposed synaptic device exhibited uniform electrical properties and small distribution of the dark current. In particular, along with the development of the optical interconnecting technology, more numbers of the optoelectronic synapses are expected to be efficiently integrated for 2
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Fig. 1. (a) Schematic of a biological synapse containing light-sensitive protein (top) and CuPc/p-6P artificial optoelectronic synapse modulated by both optical and electrical signals (bottom). (b) Molecular structures of p-6P and CuPc. Lower panel shows AFM images of CuPc and CuPc/p-6P films. (c) Energy band structures of CuPc/p-6P heterojunction before contact (i) and after contact (ii). (d) PSC responses under three types of light pulses (wavelengths = 655, 450, and 365 nm). (e) Energy band diagrams under illumination of 655 nm (or 450 nm) light pulse (left) and 365 nm light pulse (right). (f) Time-dependent change in the PSC mapping of the p-6P/CuPc structure after exposure to the 355 nm light pulse, which was detected using CSAFM, and the obtained average PSC on the surface of each state.
ΔPSC slightly increased to 0.3 nA owing to the light pulse; it was subsequently maintained for a long time, and the ΔPSC value remaining after 400 s was approximately 0.14 nA. As illustrated in the energy band diagram in Figs. 2b and S8, this positive VG decreases the hole conductance of the CuPc channel and the hole injection rate from the presynaptic terminal to the channel. Although the photogenerated holes in the p-6P layer moved to the CuPc channel, such poor hole conduction in the CuPc channel prevented a considerable increase in PSC. Here, the photogenerated electrons trapped in the p-6P layer caused the carrier distribution to be asymmetric in the channel (larger number of holes), increasing the lifetime of hole carriers and thereby maintaining the PSC for a long time. However, under VG of 0 V, the ΔPSC value increased up to 1.63 nA immediately after application of the light pulse and then maintained its initial value for a long time (approximately, 1.5 nA after 400 s and 1.36 nA even after 30 min in the inset). This is because sufficient hole conduction in the CuPc channel was achieved at the bias condition. When a negative VG was applied (−5 V), the ΔPSC increased
considerably owing to the highly conductive hole channel. However, ΔPSC continuously decreased and eventually returned to its initial value approximately 400 s after the light pulse was removed. This may be because the photogenerated electrons could not be trapped at the p6P/dielectric interface unlike in the previous two bias conditions, which probably rendered it difficult to maintain the carrier asymmetry in the channel region. We also confirmed the LTP/LTD characteristics of the optoelectronic synaptic device for successive light (365 nm, 1.0 mW, 0.5 s) and voltage pulses (−10 V, 0.1 s), as shown in Fig. 2c. When the light pulses were applied, the PSC increased linearly in a stepwise manner and it exponentially decreased for the voltage pulses. For the LTD, when a negative voltage pulse is applied to the gate terminal, holes in the CuPc layer are temporarily attracted (polarized) to the CuPc/p-6P interface, resulting in the transitory increase in the channel conductivity during the pulse duration, as shown in Figs. 2c and S9. At the same time, some of the trapped electrons also get out from the p-6P/dielectric interface and then move into the CuPc layer. 3
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Fig. 2. (a) PSC responses under the light pulse of wavelength 365 nm (1 mW, 0.5 s) with three different VG (+5, 0, and −5 V) in the CuPc/p-6P optoelectronic synapse. Insert shows the PSC response for 30 min after injecting the light pulse, where VG is 0 V. (b) Corresponding energy band diagrams of the three cases in (a). (c) PSC responses under the application of continuous light pulses (365 nm, 1 mW, 0.5 s) and voltage pulses (−10 V, 0.1 s). (d) ΔPSC for each weight updating event, which was obtained from the LTP/LTD characteristic curves in (c). (e) NL and (f) Gmax/Gmin obtained from the characteristic curves for different pulse numbers (16, 34, 70, 170, and 340). (g) Repeated synaptic characteristics for five cycles of light and voltage pulses. (h) Mean and standard deviation of the same-level current points obtained from (g). (i) NL and (j) PSC (minimum, maximum, and center) values obtained from the repeated LTP/LTD characteristic curves.
other hand, the PSC in the LTD region decreased exponentially as applying the voltage pulse. To evaluate these LTP/LTD characteristics quantitatively, we obtained the nonlinearity (NL) and maximum/ minimum conductance ratio (Gmax/Gmin) from the characteristic curves (Fig. 2e and f). NL and Gmax/Gmin are well-known key factors affecting the recognition rate of an ANN composed of synaptic devices. A lower NL and larger Gmax/Gmin of a synaptic device cause a higher recognition rate of an ANN. The NL values were obtained by fitting the measured curve to the normalized one (see the details in Fig. S11). In Fig. 2e, for the cases where 16, 34, and 70 pulses were applied, the NL values in the LTP region were close to zero (less than 0.01), which indicates that the PSC increased linearly. However, the NL in the LTD region increased in the negative direction from −2.39 to −3.59 as the number of pulses increased from 16 to 70. This is because the number of electrons escaping from the trap under the voltage stimulus decreased rapidly as the number of pulses increased, unlike in the LTP case where a similar number of electron–hole pairs was generated by the same light stimulus. In the cases where 170 and 340 pulses were applied, the PSC increased almost linearly, although the LTP curves showed nonzero NL values. This is because numerous electrons trapped by the continuous voltage pulse injections induce a large increase in the CuPc channel
Right after the pulse turns off, the holes in the CuPc channel are recombined with the electrons coming from the p-6P layer, consequently reducing the channel conductance. To analyze such variations in the PSC quantitatively, we extracted ΔPSC for each weight update event from the LTP/LTD characteristic curves (Fig. 2d). In the LTP region, the ΔPSC values were distributed near approximately 1.6 nA, regardless of the number of light pulses. This is because similar numbers of holes and electrons were generated for each light pulse in the p-6P layer. In contrast, in the LTD characteristic region, the ΔPSC decreased exponentially from 5.8 to 0.3 nA upon continuous application of voltage pulses. As the electrons trapped in the shallow energy levels at the p6P/dielectric interface can be readily de-trapped by the voltage bias, most of the electrons are predicted to be moved away from the trap sites by the first few voltage pulses. We then investigated the LTP/LTD characteristic curves for different pulse numbers (16, 34, 70, 170, and 340), as shown in Fig. S10. Here, each pulse number denotes a total number applied for the LTP/LTD. In three cases that 16, 34, and 70 pulses were applied, the PSC increased linearly in the LTP region (the ΔPSC values were almost constant). When the number of pulses increased up to 170 and 340, the ΔPSC was slightly reduced as the light pulse was continuously injected. On the 4
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pulses if more electrons are trapped. Moreover, the Gmax/Gmin value increased from 6.5 to 33.9 owing to the greater number of electron–hole pairs generated for each pulse, as the pulse power increased from 0.1 to 1.0 mW. In addition to the pulse power, we also varied the pulse width (0.1, 0.3, 0.5, and 1.0 s) and examined its effects on the LTP/LTD characteristics. Fig. 3c shows the LTP/LTD characteristic curves for each light pulse width (0.5 and 1.0 s) when applying 85 light pulses (365 nm, 1.0 mW) and 85 voltage pulses (−10 V, 0.1 s). In the LTP curves obtained by applying light pulses of width 0.5 or 1.0 s, the PSC increased linearly from 4.6 to 101.6 nA and from 3.8 to 139.7 nA, respectively, even though the ΔPSC decreased slightly after approximately 100 s. In the LTD regions for both cases, the PSC was significantly reduced by the first few voltage pulses similar to the previous case. To analyze this trend quantitatively, we obtained the NL and Gmax/Gmin values for each pulse width from the LTP/LTD characteristic curves, as shown in Fig. 3d. The NL values increased from 0.01 to 1.38 in the LTP region and decreased from −5.04 to −6.1 in the LTD region, as the pulse width increased from 0.1 to 1.0 s. The increase in pulse width appears to increase the number of electrons trapped at the p-6P/dielectric interface and thus the CuPc channel current. In addition, Gmax/Gmin increased from 5.8 to 48.2 in the same pulse width range. It is predicted that the greater numbers of electron–hole pairs generated by a light pulse with larger width provide more holes in the CuPc channel and make the p-6P/dielectric interface traps possess more electrons, eventually increasing the CuPc channel current and the Gmax/Gmin values. Based on the aforementioned results, the linearity tends to become worse but the Gmax/Gmin value increases as the power and width of input light pulse increase, which indicates that these two parameters significantly affecting the performance of the ANN are in a trade-off relationship with each other. Finally, to confirm the feasibility of the CuPc/p-6P synaptic device for ANNs, we designed a single-layer-perceptron-based ANN using its synaptic characteristics, and then performed the training/recognition tasks on MNIST digit patterns. As shown in Fig. 4a, the designed ANN consists of an input layer, an output layer, and a synapse layer connecting them, where the layers are composed of 784 input neurons, 10 output neurons, and 784 × 10 synapses, respectively. The input MNIST digit image contains 28 × 28 pixels, and each pixel has a gray scale value in the range 0–255. The values corresponding to each pixel are applied as 784 voltage signals (Vn ) to the input layer, and they are 784 transformed to 10 output currents (Im = ∑n = 1 Wn, m Vn ) through 7840 synapses (Wn, m ). Subsequently, the output value ( ym = f (Im) ) converted 1 by the sigmoid activation function ( f (Im) = ) is compared with 1 + e−βIm each label value (km ). The entire synaptic weights in this ANN are updated using the weight update method based on the backpropagation (BP) algorithm (see the details in Method section). Here, the synaptic weight is expressed as the difference between the conductance values of two equivalent synaptic devices, W = G+ − G− (Fig. 4b). This is because the conductance of a synaptic device implemented in hardware is always positive unlike synapses in software, which have both positive and negative values. In particular, we trained the ANN with unidirectional and bidirectional update methods, and compared the MNIST recognition rates of these cases. In the unidirectional update method, only G+ (or G− ) increases when W increases (or decreases): W ↑ = G+ ↑ − G− , W ↓ = G+ − G−↑. In contrast, in the bidirectional update method, G+ increases (or decreases) and simultaneously G− decreases (or increases) W when increases (or decreases): W ↑ = G+ ↑ − G−↓, W ↓ = G+ ↓ − G−↑. Thus, the unidirectional update method is more suitable for training ANNs consisting of CuPc/p-6P synaptic devices, because the NL of the LTP characteristic curve is much lower than that of the LTD curve. After we conducted the training process with 60,000 MNIST training data sets, we obtained the recognition rate every 5,000 training steps (1 epoch) with 10,000 test data sets, as shown in Fig. 4c and d. First, we determined the recognition rates for different pulse
current, thus negating the effect of holes originating from the p-6P layer. In contrast, in the LTD region, the NL increased from the abovementioned values up to −6 and −7.3 when the numbers of pulses were 170 and 340, respectively. Gmax/Gmin was also calculated for each pulse number, as shown in Fig. 2f. The Gmax/Gmin in region 1 (gray, # of pulses < 100) increased more sharply compared with that in region 2 (green, # of pulses > 100). In region 1, the Gmax/Gmin increased from 2.4 to 14.7 as the number of pulses increased from 16 to 70. The increase in Gmax/Gmin was relatively suppressed in region 2 (from 14.7 to 34.7) even though many more pulses were applied. This is because the ΔPSC started decreasing slightly after the 50th light pulse, resulting in a slow increase in Gmax. Furthermore, this CuPc/p-6P optoelectronic synaptic device exhibited stable synaptic characteristics even after many repeated measurements. We applied 35 light pulses and 35 voltage pulses per cycle to the device and monitored the LTP/ LTD characteristic curves for five cycles (Fig. 2g). We then obtained the mean and standard deviation of the same-level current points in the LTP/LTD characteristic curves over five cycles, and indicated these values by a circle and error bar, respectively, in Fig. 2h. The standard deviation for each value was very small and it was even comparable to the variation of each PSC value (ΔPSC). This indicates that our synaptic device has stable and consistent conductance states for weight update, regardless of its operating frequency. In addition, we obtained the NL and PSC (minimum, maximum, and intermediate) values from the LTP/ LTD characteristic curves in each cycle (Fig. 2i and j, respectively). The NL values in the LTP and LTD regions remained approximately 0.01 and 3.5 for the five cycles, respectively. The PSCmax, PSCmin, and PSCcenter values remained at approximately 49.9, 4.4, and 27.2 nA, respectively. These results indicate that this synaptic device has stable synaptic characteristics for a reliable weight update. We investigated the LTP/LTD characteristics of the synaptic device again under various light pulse conditions (Figs. S14 and S15). This is because the weight of synapses in ANNs is based on the LTP/LTD characteristic curves, which are strongly dependent on the light pulse condition. To examine the LTP characteristic, we applied light pulses of various powers (0.1, 0.4, 0.7, and 1.0 mW) with the width and wavelength of 0.5 s and 365 nm, respectively. The voltage pulse of magnitude −10 V and width 0.1 s was used for the analysis of the LTD characteristic. Fig. 3a shows the LTP/LTD characteristic curves obtained after applying 85 light pulses and 85 voltage pulses. For the power of 0.1 mW, the PSC value increased linearly from 3.3 to 17.9 nA in the LTP region and decreased nonlinearly to the initial value in the LTD region. The ΔPSC values remained constant at approximately 0.173 nA in the LTP region, but the values in the LTD region were distributed between 0.05 and 3.1 nA (Fig. S16). The stable PSC change in the LTP region indicates that the synaptic weight can be consistently updated by the light pulse. In contrast, the high ΔPSC variation in the LTD curve is predicted to cause an uneven weight update problem. When the light pulses of power 0.4 mW were applied, the PSC increased from 4.5 to 51.8 nA more rapidly, compared with the case of pulse power of 0.1 mW and then decreased to the initial value. This is because, as the power of light pulses increases, the number of holes moving to the CuPc channel increases, also increasing the number of electrons trapped at the p-6P/dielectric interface. We then obtained the NL and Gmax/Gmin values for each pulse power from the LTP/LTD characteristic curve (Fig. 3b). For the pulse powers of 0.1 and 0.4 mW, the NL values in the LTP region were close to 0 (linear increase of PSC). This is because similar numbers of holes move to the CuPc channel at every light pulse, regardless of the pulse number. As the pulse power increased to 0.7 and 1.0 mW, the NL slightly increased to 0.21 and 0.5, respectively (nonlinear increase of PSC). This can be explained by the numerous trapped electrons causing a larger increase in the channel current, compared with that by the additional holes moving from the p6P layer. In the LTD region, the NL increased in a negative direction from −4.6 to −6.1 as the pulse power increased from 0.1 to 1.0 mW. This is because more electrons are released in the first few voltage 5
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Fig. 3. LTP/LTD characteristics of the CuPc/p-6P optoelectronic synapse under various light pulse conditions: (a) power and (c) width. NL and Gmax/Gmin obtained from the LTP/LTD characteristic curves for different (b) light pulse powers and (d) light pulse widths.
Fig. 4. (a) Schematic illustration of single-layer-perceptron-based ANN with 784 input neurons and 10 output neurons fully connected through 7840 synaptic weights. (b) Representation of synaptic weight using the conductance difference between two equivalent optoelectronic synaptic devices (left) and detailed weight updating processes based on unidirectional and bidirectional update methods (right). (c) Recognition rate as a function of the number of training epochs for the cases with various light pulse conditions. (d) Recognition rate as a function of the number of training epochs for the cases where unidirectional and bidirectional update methods are used. 6
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p-6P films were deposited thermally on the SiO2 surface at a rate of 1 nm/min. During the deposition, the substrate temperature was set to 180 °C. Subsequently, the source and drain electrodes (thickness = 40 nm) were formed by evaporating gold through a shadow mask on top of the CuPc layer. The width and length of the active channel were 1000 and 100 μm, respectively. The current-sensing atomic force microscopy (CSAFM) measurement was performed using an Agilent Technologies 5500 AFM/SPM System equipped with conductive Pt–Ir coated tips (Bruker Nano Inc). The electrical properties of the optoelectronic synapse were measured using a semiconductor parameter characterization system (Keithley 4200) in ambient condition and at room temperature. A laser diode was used as the light source, which was controlled by a function/arbitrary waveform generator (Agilent 33220A) to generate the light pulse. The light power was adjusted from 0.1 to 1 mW (light power intensity from 0.13 to 1.27 mW/cm2), which was calibrated using an optical power meter (Thorlabs PM 100D). In the weight update method based on the hardware-based backpropagation algorithm, as the measured conductance states always have positive values, the synaptic weight is represented as the difference between the conductance values of two equivalent synaptic devices (W = G+ − G−). To determine whether the synaptic weight was potentiated or depressed, we calculated the sign of ΔW (sgn(ΔW)) using the difference between the output value ( ym ) and each label value (km ):
width (solid line, 1.0 mW) and power (dashed line, 0.5 s) conditions using the bidirectional update method (Fig. 4c). After learning for the 3rd epoch, the recognition rates were saturated, where the highest value appeared to be approximately 68% in the case of pulse width 1.0 s. As Gmax/Gmin decreased in the order of the corresponding pulse conditions #2→#4→#3→#1 (inset table), the recognition rate also decreased from 67% to 43%. This indicates that Gmax/Gmin had a greater effect than NL (|NLP|+|NLD|) on the recognition rate for MNIST digit patterns. Subsequently, to maximize the effect of the superior linearity in the LTP region, we applied the unidirectional update method to the training tasks for two cases of pulses i.e., condition #2 with 1.0 s and 1.0 mW (solid line) and condition #4 with 0.5 s and 1.0 mW (dashed line). As shown in Fig. 4d, the recognition rates predicted using the unidirectional update method (blue) were higher than those of the bidirectional case (red). For the pulse conditions #2 and #4, the recognition rates were 78% and 73%, respectively. This is because the LTP characteristic curve showing better linearity was only used for updating weights, thereby improving the effective NL (7.49 → 1.38 and 6.57 → 0.5). We also obtained the recognition rates for various pulse conditions, with pulse widths of 0.3, 0.5, and 1.0 s, and pulse powers of 0.4, 0.7, and 1.0 mW (Fig. S17). Similar to the above trend, the cases where the unidirectional update method was used presented higher recognition rates under all pulse conditions. Through this supervised learning simulation for MNIST digit patterns, we confirmed that the recognition rate of the ANN consisting of the proposed optoelectronic synaptic devices increased up to 78% with the use of the unidirectional update method. Through this supervised learning simulation for MNIST digit patterns, we confirmed that the recognition rate of the ANN consisting of the proposed optoelectronic synaptic devices increased up to 78% with the use of the unidirectional update method. In order to show the possibility to improve the recognition rate further, we configured a 400 × 200 × 10 double-layer ANN using the LTP/LTD characteristic curves obtained under various light pulse conditions and then performed the training/recognition tasks for the cropped MNIST pattern data (20 × 20) [36,37]. As a result, the maximum recognition rate reached up to 87.11% for the case using the pulse power of 0.4 mW (Fig. S19 and Table S1).
sgn(ΔW) > 0 if km-ym > 0 (potentiation),
(1)
sgn(ΔW) < 0 if km-ym < 0 (depression).
(2)
For potentiating the synaptic weight (sgn(ΔW) > 0), G+ was increased and G− was decreased simultaneously (W ↑ = G+ ↑ − G−↓). In contrast, for the depression (sgn(ΔW) < 0), G+ was decreased and G− was increased simultaneously (W ↓ = G+ ↓ − G−↑). When the conductance of the synaptic device reached the maximum value (Gmax ), both G+ and G− were initialized to Gmin (refresh), and the larger of the two was increased again by the difference between G+ and G− (reprogram). As mentioned in the main text, this is called the bidirectional update method, where both conductance states in the long-term potentiation (LTP) and long-term depression (LTD) characteristics curves are used for updating. The unidirectional update method is different in that it uses only the LTP characteristic. When potentiating the synaptic weight (sgn(ΔW) > 0), only G+ was increased (W ↑ = G+ ↑ − G− ), whereas for the depression of the weight (sgn(ΔW) < 0), only G− was increased (W ↓ = G+ − G−↑).
3. Conclusion In conclusion, we reported an optoelectronic synaptic device fabricated on a CuPc/p-6P heterojunction. During the operation of this synaptic device, photogenerated holes moved to the CuPc channel and photogenerated electrons were trapped at the p-6P/dielectric interface, thereby causing a consistent increase in the channel current. Basic synaptic characteristics of this synaptic device, such as PSC and LTP/LTD, were successfully demonstrated by using light and voltage pulses for potentiating and depressing the synaptic weight, respectively. Particularly, the NL in the LTP characteristic curve was less than 0.01, which was close to the ideal value for the training process of an ANN. The maximum recognition rate for MNIST digit patterns was as high as 78% through the use of the unidirectional update method with only the LTP characteristic, even though the ANN was based on a single-layer perceptron model. As the number of electron–hole pairs generated by a light pulse was constant, the numbers of holes supplied to the CuPc channel and electrons trapped at the p-6P/dielectric interface were also similar at every light pulse injection, resulting in a linear increase in the channel conductance. The proposed synaptic device is expected to play a significant role in the progress of synaptic device technology using optoelectronic spiking signals.
Acknowledgement The authors acknowledge the grants from Basic Science Research Program and Nano Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B2005790, 2016M3A7B4910426, and 2017R1A4A1015400). J.S. acknowledges support by the National Natural Science Foundation of China (61975241). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.nanoen.2019.104095. References [1] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Nature 529 (2016) 484. [2] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis, Science 362
4. Methods CuPc and p-6P were purchased from Sigma-Aldrich. A heavily doped n-type silicon substrate with a thermally grown 200-nm-thick SiO2 gate dielectric was utilized as the bottom gate. Further, 30 nm CuPc/7.5 nm 7
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Dr. Chuan Qian received his B.S. in Applied Physics from Xinjiang University in 2012 and Ph.D. degree in Physics from Central South University in 2017. Afterwards, he worked as a postdoctoral researcher at Sungkyunkwan University (SKKU) in 2018. Now, he is a postdoctoral researcher in Jeong Ho Cho's group at Yonsei University. His research interest includes artificial synaptic devices.
(2018) 1140–1144. [3] G. Indiveri, S.-C. Liu, Proc. IEEE Inst. Electr. Electron. Eng. 103 (2015) 1379–1397. [4] C.K. Machens, Science 338 (2012) 1156–1157. [5] R.A. Nawrocki, R.M. Voyles, S.E. Shaheen, IEEE Trans. Electron Devices 63 (2016) 3819–3829. [6] W. Zhang, R. Mazzarello, M. Wuttig, E. Ma, Nat. Rev. Mater. 4 (2019) 150–168. [7] S. Yu, Y. Wu, R. Jeyasingh, D. Kuzum, H.-S.P. Wong, IEEE Trans. Electron Devices 58 (2011) 2729–2737. [8] M. Prezioso, F. Merrikh-Bayat, B.D. Hoskins, G.C. Adam, K.K. Likharev, D.B. Strukov, Nature 521 (2015) 61. [9] H.-S.P. Wong, S. Raoux, S. Kim, J. Liang, J.P. Reifenberg, B. Rajendran, M. Asheghi, K.E. Goodson, Proc. IEEE Inst. Electr. Electron. Eng. 98 (2010) 2201–2227. [10] H. Wang, Q. Zhao, Z. Ni, Q. Li, H. Liu, Y. Yang, L. Wang, Y. Ran, Y. Guo, W. Hu, Y. Liu, Adv. Mater. 30 (2018) 1803961. [11] M.-K. Kim, J.-S. Lee, Nano Lett. 19 (2019) 2044–2050. [12] Y. van de Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria, S. Agarwal, M.J. Marinella, A. Alec Talin, A. Salleo, Nat. Mater. 16 (2017) 414. [13] C. Qian, J. Sun, L.-A. Kong, G. Gou, J. Yang, J. He, Y. Gao, Q. Wan, ACS Appl. Mater. Interfaces 8 (2016) 26169–26175. [14] P. Gkoupidenis, N. Schaefer, B. Garlan, G.G. Malliaras, Adv. Mater. 27 (2015) 7176–7180. [15] F. Yu, L.Q. Zhu, H. Xiao, W.T. Gao, Y.B. Guo, Adv. Funct. Mater. 28 (2018) 1804025. [16] Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh, D. Son, J. Kang, A.M. Foudeh, C. Zhu, Y. Lee, S. Niu, J. Liu, R. Pfattner, Z. Bao, T.-W. Lee, Science 360 (2018) 998–1003. [17] S. Kim, J. Yoon, H.-D. Kim, S.-J. Choi, ACS Appl. Mater. Interfaces 7 (2015) 25479–25486. [18] Y. Wang, Z. Lv, J. Chen, Z. Wang, Y. Zhou, L. Zhou, X. Chen, S.-T. Han, Adv. Mater. 30 (2018) 1802883. [19] S.J. Martin, P.D. Grimwood, R.G.M. Morris, Annu. Rev. Neurosci. 23 (2000) 649–711. [20] S. Kim, B. Choi, M. Lim, J. Yoon, J. Lee, H.-D. Kim, S.-J. Choi, ACS Nano 11 (2017) 2814–2822. [21] E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal, Y. Li, Y. Tuchman, C.D. James, M.J. Marinella, J.J. Yang, A. Salleo, A.A. Talin, Science 364 (2019) 570–574. [22] E. Pastrana, Nat. Methods 8 (2011) 24–25. [23] A.D. Douglass, S. Kraves, K. Deisseroth, A.F. Schier, F. Engert, Curr. Biol. 18 (2008) 1133–1137. [24] D. Huber, L. Petreanu, N. Ghitani, S. Ranade, T. Hromádka, Z. Mainen, K. Svoboda, Nature 451 (2008) 61–64. [25] L. Yin, C. Han, Q. Zhang, Z. Ni, S. Zhao, K. Wang, D. Li, M. Xu, H. Wu, X. Pi, D. Yang, Nano Energy 63 (2019) 103859. [26] S. Seo, S.-H. Jo, S. Kim, J. Shim, S. Oh, J.-H. Kim, K. Heo, J.-W. Choi, C. Choi, S. Oh, D. Kuzum, H.-S.P. Wong, J.-H. Park, Nat. Commun. 9 (2018) 5106. [27] Y. Lee, J.Y. Oh, W. Xu, O. Kim, T.R. Kim, J. Kang, Y. Kim, D. Son, J.B.-H. Tok, M.J. Park, Z. Bao, T.-W. Lee, Sci. Adv. 4 (2018) eaat7387. [28] J. Sun, S. Oh, Y. Choi, S. Seo, M.J. Oh, M. Lee, W.B. Lee, P.J. Yoo, J.H. Cho, J.H. Park, Adv. Funct. Mater. 28 (2018) 1804397. [29] S. Ham, S. Choi, H. Cho, S.-I. Na, G. Wang, Adv. Funct. Mater. 10 (2018) 1806646. [30] J. Jiang, W. Hu, D. Xie, J. Yang, J. He, Y. Gao, Q. Wan, Nanoscale 11 (2019) 1360–1369. [31] Y. Chen, W. Qiu, X. Wang, W. Liu, J. Wang, G. Dai, Y. Yuan, Y. Gao, J. Sun, Nano Energy 62 (2019) 393–400. [32] C. Qian, J. Sun, L. Zhang, H. Huang, J. Yang, Y. Gao, J. Phys. Chem. C 119 (2015) 14965–14971. [33] K.M. Lau, J.X. Tang, H.Y. Sun, C.S. Lee, S.T. Lee, D. Yan, Appl. Phys. Lett. 88 (2006) 173513. [34] N. Koch, E. Zojer, A. Rajagopal, J. Ghjisen, R.L. Johnson, G. Leising, J.-J. Pireaux, Adv. Funct. Mater. 11 (2001) 51–58. [35] A. Novikov, Solid State Electron. 54 (2010) 8–13. [36] P. Chen, X. Peng, S. Yu, IEEE International Electron Devices Meeting (IEDM), (2017), pp. 6.1.1–6.1.4. [37] P. Chen, X. Peng, S. Yu, IEEE Trans. CAD 37 (2018) 3067–3080.
Seyong Oh received his BS in the Department of Electronic and Electrical Engineering from SKKU in 2015. Now he is Ph.D. candidate in Jin-Hong Park's group at Department of Electrical and Computer Engineering from SKKU. His research interest includes neuromorphic devices and circuits.
Prof. Jin-Hong Park has been an associate professor with School of Electronic and Electrical Engineering and SKKU Advanced Institute of Nano Technology in SKKU since 2011. From 2009 to 2011, he had been Kyung Hee University and IBM Thomas J. Watson Research Center in Yorktown Heights, NY. He received M.S./Ph.D. degree in Electrical Engineering from Stanford University in 2006 and 2009, and B.S. degree in Electrical Engineering from SKKU in 2004. His current research focuses on (1) next generation low-power devices based on the 2D vdW materials (multi-valued logic devices and neuromorphic devices) and (2) 2D vdW material-based process technologies/devices.
Prof. Jeong Ho Cho obtained his B.S. in chemical engineering from Sogang University in 2001 and his MS and Ph.D. in Chemical Engineering from POSTECH in 2006. He was a postdoctoral researcher in Department of Chemical Engineering and Materials Science at University of Minnesota (2006–2008) and then joined as a faculty at Soongsil University (2008–2012) and Sunkyunkwan University (2012–2018). He now is a professor at Yonsei University with an appointment in Department of Chemical and Biomolecular Engineering. His research interests include organic electronic devices (transistor, memory, and sensor) and 2-dimensional nanomaterials.
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Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing
T
Chuan Qiana,b,1, Seyong Ohc,1, Yongsuk Choib, Jeong-Hoon Kimc, Jia Sund, Han Huangd, Junliang Yangd, Yongli Gaod,e, Jin-Hong Parkb,c,∗∗, Jeong Ho Choa,∗ a
Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 120-749, Republic of Korea SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea c Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea d Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, 410083, PR China e Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: Solar-stimulated optoelectronic synapse Neuromorphic computing Organic heterojunction Band engineering Pattern recognition
We report an artificial optoelectronic synapse based on a copper-phthalocyanine (CuPc) and para-sexiphenyl (p6P) heterojunction structure. This device features stable conductance states and their linear distribution in longterm potentiation (LTP) characteristic curve formed by continuous input light pulses. These superior synaptic characteristics originate from the fact that the number of photo-holes moving into the CuPc channel and photoelectrons being trapped at the p-6P/dielectric interface is constant at every light pulse. A single-layer neural network is theoretically formed with these optoelectronic synaptic devices and its feasibility is studied in terms of training/recognition tasks of the Modified National Institute of Standards and Technology digit image patterns. Owing to the excellent LTP characteristic and through the use of a unidirectional update method, its maximum recognition rate is as high as 78% despite the use of a single-layer network. This study is expected to provide a foundation for future studies on optoelectronic synaptic devices toward the implementation of complex artificial neural networks.
1. Introduction In the field of artificial intelligence, AlphaGo, seeking hegemony in the game of Go, has attracted considerable interest [1]. Recently, through self-play, AlphaZero defeated a world champion program in the game of chess [2]. The learning algorithm for an artificial neural network (ANN) based on von Neumann computing has evolved rapidly and is a leading technological trend [3,4]. However, these conventional digital computing systems are configured with separate data-computing (CPU) and data-storing (memory) units, where the data processing for learning complex ANNs is limited by the data exchange rate [5,6]. To overcome this bottleneck, a bio-inspired electronic device, called “synaptic device,” which can process and memorize information simultaneously, has recently attracted significant attention. So far, many types of devices have been proposed as artificial synaptic devices, such as memristors [7,8], phase change memory [6,9], ferroelectric transistors [10,11], electrochemical transistors [12–16], and field-effect transistors
[17,18]. In a biological synapse, synaptic weight is persistently strengthened or weakened by signal transmission between presynaptic and postsynaptic neurons, resulting in long-term potentiation (LTP) or long-term depression (LTD), respectively [19]. For successful implementation of these synaptic dynamics, a linear weight update with sufficient conductance states is essential [20,21]. However, in many prior studies on electronic synaptic devices, synaptic response induced by continuous voltage pulses was different in each pulse and each cycle, which has hindered the linear and uniform modulation of the conductance states. As a nonlinear weight update is one of the major obstacles in implementing complex ANNs composed of synaptic devices, the studies on synaptic devices with highly linear LTP/LTD characteristics must be conducted. In contrast to electrical signals, light uses a wireless medium without transmission loss, and it is even used to control cells with lightsensitive protein in living tissues of nematodes, fruit flies, mice, etc [22–24]. Moreover, processing of visual detail as sight in a visual
∗
Corresponding author. Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 120-749, Republic of Korea. Corresponding author. SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea. E-mail addresses: [email protected] (J.-H. Park), [email protected] (J.H. Cho). 1 The authors contribute equally to this paper. ∗∗
https://doi.org/10.1016/j.nanoen.2019.104095 Received 9 July 2019; Received in revised form 2 September 2019; Accepted 3 September 2019 Available online 06 September 2019 2211-2855/ © 2019 Elsevier Ltd. All rights reserved.
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system-level ANN. The numerical information required for aligning the energy bands of CuPc, p-6P, SiO2, and Si is shown in Fig. S3 [33–35]. Under equilibrium state (VG = 0 V), some electrons are trapped in the p6P layer near the interface with the dielectric layer and hole carriers appear to be slightly accumulated near the interface with the p-6P layer. When three types of light pulses (wavelengths = 655, 450, and 365 nm) were applied to the CuPc/p-6P optoelectronic synapse, we could observe different postsynaptic current (PSC) responses, as shown in Fig. 1d. The light pulse signals were absorbed in the heterojunction channel and were transformed to electrical signals via an electron–hole generation process. When a 655 nm light pulse of power 1.0 mW was applied, the PSC increased up to 4.64 nA; however, it returned to its initial current level after approximately 2 s. This is because the photogenerated carriers instantly increased the channel conductance and the carrier injection rate from the presynaptic terminal, and they rapidly recombined with each other. Under the illumination of a 655 nm light pulse, electron and hole carriers are readily generated because the photon energy (Eph = 1.89 eV) is larger than the bandgap of CuPc (1.65 eV). These carriers quickly recombine because there are numerous electrons and holes in the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) bands, respectively. Under the illumination of a 450 nm light pulse (Eph = 2.76 eV), a similar response was observed, but its peak PSC value (4.52 nA) was smaller than that in the previous case. This may be because both CuPc and p-6P present relatively low light absorption in the wavelength region near 450 nm. When a 365-nm light pulse was applied, a larger PSC (its peak value was 4.67 nA) was observed despite lower power (P = 0.1 mW), compared with the previous two cases (655- and 450-nm lights with P = 1.0 mW). Especially, the PSC was reduced slightly and maintained over 10 s (approximately 4.64 nA). The smaller decay of the PSC is because the carriers generated by the 365nm light pulse have a relatively longer lifetime (lower recombination rate), compared to the other cases. As shown in Fig. 1e, under VG = 0 V, the holes generated in the p-6P layer are predicted to move to the CuPc channel region and the remaining electrons are probably trapped at the p-6P/dielectric interface. The absence of holes in the p-6P region and the additional supply of electrons into the CuPc region cause an asymmetric carrier distribution, thereby reducing the carrier recombination rate. Of course, carrier traps such as imperfectly terminated bonds and grain boundaries will increase the recombination rate of carriers. However, the amount of the traps is predicted to be not much large because the PSC was stably maintained for a long time after the light pulse disappeared (Fig. 1d). In addition, the time-dependent change in the average PSC on the CuPc surface of the p-6P/CuPc structure was observed using in situ CSAFM after exposure to the 365 nm light pulse. As shown in Fig. 1f, the PSC increased from 42.3 (dark brown) to 81.9 pA (light yellow) under the light illumination. When the light was turned off, the PSC was reduced to 65.2 pA after 10 min, to 53.3 pA after 20 min, and then to 49.6 pA after 30 min. Approximately 61% of the peak current value was retained even after 30 min because the holes transferred from the p-6P layer cause an asymmetric carrier distribution in the CuPc and consequently stay in the HOMO band of the CuPc for a long time with fewer recombinations. However, for the device only composed of a CuPc film, the PSC returned quickly to its initial current level (Figs. S5 and S6). This is presumably because the electrons and holes generated in the CuPc film recombined in a short time owing to their symmetric distribution. As this CuPc/p-6P synaptic device was based on a phototransistor with charge-storing characteristic, we could control its synaptic weight (conductivity) by using both light and gate voltage simultaneously. When a light pulse (365 nm, 1.0 mW, 0.5 s) was applied to this device under three different gate voltages (+5, 0, and −5 V), we observed different synaptic current responses, as shown in Figs. 2a and S7. Here, ΔPSC was defined as the difference between postsynaptic currents before and after the light illumination. When VG of +5 V was applied,
system is a unique and important part in a biological neural network, where the retina is a light-sensitive layer transforming light signals to electrical pulses, similar to a photodetector. Thus, it is meaningful to implement a synaptic device enabling the transformation of light signals to electrical information. Until now, many studies on artificial optoelectronic synapses have been reported [18,25–31]. Lee's group mimicked a sensorimotor nervous system [27], and Wang's group fabricated two terminal optoelectronic synapses based on organolead-halide-perovskite [29]. In addition, Prof. Su-Ting Han's group emulated the basic optosynaptic behaviors in a type-II heterojunction device and measured the dynamic states of carrier redistribution using a Kelvin probe force microscope [18]. However, these studies mainly focused on the basic synaptic dynamics, and few studies showed the application to ANNs. Therefore, the feasibility of optoelectronic synapses toward complex neural networks must be studied further. Here, we demonstrate an artificial optoelectronic synapse achieved through band engineering of an organic heterojunction and then confirm its feasibility for ANNs in terms of the training/recognition tasks of the Modified National Institute of Standards and Technology (MNIST) digit image patterns. The heterojunction consists of copper-phthalocyanine (CuPc) and para-sexiphenyl (p-6P) layers [32]. Under various 365 nm UV light pulse conditions, solid synaptic responses (LTP) were observed using in situ current-sensing atomic force microscopy (CSAFM) and electrical measurements. In particular, conductance states varied by the continuous light pulses were stable and linearly potentiated, accordingly enhancing the recognition rates for the MNIST digit patterns with the assistance of a unidirectional update method. This perfectly linear LTP characteristic originates from the fact that the numbers of holes moving into the CuPc channel and electrons being trapped at the p-6P/dielectric interface are constant at every light pulse. 2. Results and discussion Biological synapses containing light-sensitive proteins are known to receive and transmit light signals, in addition to electric signals [22–24]. To mimic such optosynaptic dynamics, we fabricated an artificial optoelectronic synaptic device by using a CuPc/p-6P organic heterojunction. Fig. 1a shows the schematic diagram of the synaptic device, which uses both optical and electrical signals to modulate its channel conductivity. Here, the source and drain electrodes are defined as pre- and post-synaptic terminals, respectively. As the channel conductivity is directly related to the synaptic weight of a biological synapse, the synaptic weight is strengthened by a light pulse illuminated on top of the device and it is weakened by a back-gate voltage pulse. In this optoelectronic synaptic device, the active channel consisting of the CuPc/p-6P heterojunction is the most important part. This is because the CuPc/p-6P thin films are very sensitive to light, thus, providing a foundation for finely controlling the conductivity with a low-energy light pulse. In the heterojunction channel, the p-6P layer was grown in the form of discontinuous islands based on the Stranski–Krastanov growth mechanism; however, it improved the morphology of the overlying CuPc thin film with ordered arrangement and also improved its electrical conductivity (Fig. 1b and Fig. S1). Particularly, owing to the discontinuous crystal formation and large energy bandgap of the p6P layer, an effective carrier channel is expected to be formed in the CuPc layer. Consequently, this CuPc/p-6P heterojunction channel with a properly aligned band structure (Fig. 1c) not only considerably improved the photoresponse of the synaptic device (Fig. S1), but also enabled the implementation of the most essential feature for nonvolatile LTP/LTD characteristics. In order to confirm the uniformity of our organic synaptic devices fabricated onto 2 × 2 cm2, the device-todevice variation was investigated. As shown in Fig. S2, the proposed synaptic device exhibited uniform electrical properties and small distribution of the dark current. In particular, along with the development of the optical interconnecting technology, more numbers of the optoelectronic synapses are expected to be efficiently integrated for 2
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Fig. 1. (a) Schematic of a biological synapse containing light-sensitive protein (top) and CuPc/p-6P artificial optoelectronic synapse modulated by both optical and electrical signals (bottom). (b) Molecular structures of p-6P and CuPc. Lower panel shows AFM images of CuPc and CuPc/p-6P films. (c) Energy band structures of CuPc/p-6P heterojunction before contact (i) and after contact (ii). (d) PSC responses under three types of light pulses (wavelengths = 655, 450, and 365 nm). (e) Energy band diagrams under illumination of 655 nm (or 450 nm) light pulse (left) and 365 nm light pulse (right). (f) Time-dependent change in the PSC mapping of the p-6P/CuPc structure after exposure to the 355 nm light pulse, which was detected using CSAFM, and the obtained average PSC on the surface of each state.
ΔPSC slightly increased to 0.3 nA owing to the light pulse; it was subsequently maintained for a long time, and the ΔPSC value remaining after 400 s was approximately 0.14 nA. As illustrated in the energy band diagram in Figs. 2b and S8, this positive VG decreases the hole conductance of the CuPc channel and the hole injection rate from the presynaptic terminal to the channel. Although the photogenerated holes in the p-6P layer moved to the CuPc channel, such poor hole conduction in the CuPc channel prevented a considerable increase in PSC. Here, the photogenerated electrons trapped in the p-6P layer caused the carrier distribution to be asymmetric in the channel (larger number of holes), increasing the lifetime of hole carriers and thereby maintaining the PSC for a long time. However, under VG of 0 V, the ΔPSC value increased up to 1.63 nA immediately after application of the light pulse and then maintained its initial value for a long time (approximately, 1.5 nA after 400 s and 1.36 nA even after 30 min in the inset). This is because sufficient hole conduction in the CuPc channel was achieved at the bias condition. When a negative VG was applied (−5 V), the ΔPSC increased
considerably owing to the highly conductive hole channel. However, ΔPSC continuously decreased and eventually returned to its initial value approximately 400 s after the light pulse was removed. This may be because the photogenerated electrons could not be trapped at the p6P/dielectric interface unlike in the previous two bias conditions, which probably rendered it difficult to maintain the carrier asymmetry in the channel region. We also confirmed the LTP/LTD characteristics of the optoelectronic synaptic device for successive light (365 nm, 1.0 mW, 0.5 s) and voltage pulses (−10 V, 0.1 s), as shown in Fig. 2c. When the light pulses were applied, the PSC increased linearly in a stepwise manner and it exponentially decreased for the voltage pulses. For the LTD, when a negative voltage pulse is applied to the gate terminal, holes in the CuPc layer are temporarily attracted (polarized) to the CuPc/p-6P interface, resulting in the transitory increase in the channel conductivity during the pulse duration, as shown in Figs. 2c and S9. At the same time, some of the trapped electrons also get out from the p-6P/dielectric interface and then move into the CuPc layer. 3
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Fig. 2. (a) PSC responses under the light pulse of wavelength 365 nm (1 mW, 0.5 s) with three different VG (+5, 0, and −5 V) in the CuPc/p-6P optoelectronic synapse. Insert shows the PSC response for 30 min after injecting the light pulse, where VG is 0 V. (b) Corresponding energy band diagrams of the three cases in (a). (c) PSC responses under the application of continuous light pulses (365 nm, 1 mW, 0.5 s) and voltage pulses (−10 V, 0.1 s). (d) ΔPSC for each weight updating event, which was obtained from the LTP/LTD characteristic curves in (c). (e) NL and (f) Gmax/Gmin obtained from the characteristic curves for different pulse numbers (16, 34, 70, 170, and 340). (g) Repeated synaptic characteristics for five cycles of light and voltage pulses. (h) Mean and standard deviation of the same-level current points obtained from (g). (i) NL and (j) PSC (minimum, maximum, and center) values obtained from the repeated LTP/LTD characteristic curves.
other hand, the PSC in the LTD region decreased exponentially as applying the voltage pulse. To evaluate these LTP/LTD characteristics quantitatively, we obtained the nonlinearity (NL) and maximum/ minimum conductance ratio (Gmax/Gmin) from the characteristic curves (Fig. 2e and f). NL and Gmax/Gmin are well-known key factors affecting the recognition rate of an ANN composed of synaptic devices. A lower NL and larger Gmax/Gmin of a synaptic device cause a higher recognition rate of an ANN. The NL values were obtained by fitting the measured curve to the normalized one (see the details in Fig. S11). In Fig. 2e, for the cases where 16, 34, and 70 pulses were applied, the NL values in the LTP region were close to zero (less than 0.01), which indicates that the PSC increased linearly. However, the NL in the LTD region increased in the negative direction from −2.39 to −3.59 as the number of pulses increased from 16 to 70. This is because the number of electrons escaping from the trap under the voltage stimulus decreased rapidly as the number of pulses increased, unlike in the LTP case where a similar number of electron–hole pairs was generated by the same light stimulus. In the cases where 170 and 340 pulses were applied, the PSC increased almost linearly, although the LTP curves showed nonzero NL values. This is because numerous electrons trapped by the continuous voltage pulse injections induce a large increase in the CuPc channel
Right after the pulse turns off, the holes in the CuPc channel are recombined with the electrons coming from the p-6P layer, consequently reducing the channel conductance. To analyze such variations in the PSC quantitatively, we extracted ΔPSC for each weight update event from the LTP/LTD characteristic curves (Fig. 2d). In the LTP region, the ΔPSC values were distributed near approximately 1.6 nA, regardless of the number of light pulses. This is because similar numbers of holes and electrons were generated for each light pulse in the p-6P layer. In contrast, in the LTD characteristic region, the ΔPSC decreased exponentially from 5.8 to 0.3 nA upon continuous application of voltage pulses. As the electrons trapped in the shallow energy levels at the p6P/dielectric interface can be readily de-trapped by the voltage bias, most of the electrons are predicted to be moved away from the trap sites by the first few voltage pulses. We then investigated the LTP/LTD characteristic curves for different pulse numbers (16, 34, 70, 170, and 340), as shown in Fig. S10. Here, each pulse number denotes a total number applied for the LTP/LTD. In three cases that 16, 34, and 70 pulses were applied, the PSC increased linearly in the LTP region (the ΔPSC values were almost constant). When the number of pulses increased up to 170 and 340, the ΔPSC was slightly reduced as the light pulse was continuously injected. On the 4
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pulses if more electrons are trapped. Moreover, the Gmax/Gmin value increased from 6.5 to 33.9 owing to the greater number of electron–hole pairs generated for each pulse, as the pulse power increased from 0.1 to 1.0 mW. In addition to the pulse power, we also varied the pulse width (0.1, 0.3, 0.5, and 1.0 s) and examined its effects on the LTP/LTD characteristics. Fig. 3c shows the LTP/LTD characteristic curves for each light pulse width (0.5 and 1.0 s) when applying 85 light pulses (365 nm, 1.0 mW) and 85 voltage pulses (−10 V, 0.1 s). In the LTP curves obtained by applying light pulses of width 0.5 or 1.0 s, the PSC increased linearly from 4.6 to 101.6 nA and from 3.8 to 139.7 nA, respectively, even though the ΔPSC decreased slightly after approximately 100 s. In the LTD regions for both cases, the PSC was significantly reduced by the first few voltage pulses similar to the previous case. To analyze this trend quantitatively, we obtained the NL and Gmax/Gmin values for each pulse width from the LTP/LTD characteristic curves, as shown in Fig. 3d. The NL values increased from 0.01 to 1.38 in the LTP region and decreased from −5.04 to −6.1 in the LTD region, as the pulse width increased from 0.1 to 1.0 s. The increase in pulse width appears to increase the number of electrons trapped at the p-6P/dielectric interface and thus the CuPc channel current. In addition, Gmax/Gmin increased from 5.8 to 48.2 in the same pulse width range. It is predicted that the greater numbers of electron–hole pairs generated by a light pulse with larger width provide more holes in the CuPc channel and make the p-6P/dielectric interface traps possess more electrons, eventually increasing the CuPc channel current and the Gmax/Gmin values. Based on the aforementioned results, the linearity tends to become worse but the Gmax/Gmin value increases as the power and width of input light pulse increase, which indicates that these two parameters significantly affecting the performance of the ANN are in a trade-off relationship with each other. Finally, to confirm the feasibility of the CuPc/p-6P synaptic device for ANNs, we designed a single-layer-perceptron-based ANN using its synaptic characteristics, and then performed the training/recognition tasks on MNIST digit patterns. As shown in Fig. 4a, the designed ANN consists of an input layer, an output layer, and a synapse layer connecting them, where the layers are composed of 784 input neurons, 10 output neurons, and 784 × 10 synapses, respectively. The input MNIST digit image contains 28 × 28 pixels, and each pixel has a gray scale value in the range 0–255. The values corresponding to each pixel are applied as 784 voltage signals (Vn ) to the input layer, and they are 784 transformed to 10 output currents (Im = ∑n = 1 Wn, m Vn ) through 7840 synapses (Wn, m ). Subsequently, the output value ( ym = f (Im) ) converted 1 by the sigmoid activation function ( f (Im) = ) is compared with 1 + e−βIm each label value (km ). The entire synaptic weights in this ANN are updated using the weight update method based on the backpropagation (BP) algorithm (see the details in Method section). Here, the synaptic weight is expressed as the difference between the conductance values of two equivalent synaptic devices, W = G+ − G− (Fig. 4b). This is because the conductance of a synaptic device implemented in hardware is always positive unlike synapses in software, which have both positive and negative values. In particular, we trained the ANN with unidirectional and bidirectional update methods, and compared the MNIST recognition rates of these cases. In the unidirectional update method, only G+ (or G− ) increases when W increases (or decreases): W ↑ = G+ ↑ − G− , W ↓ = G+ − G−↑. In contrast, in the bidirectional update method, G+ increases (or decreases) and simultaneously G− decreases (or increases) W when increases (or decreases): W ↑ = G+ ↑ − G−↓, W ↓ = G+ ↓ − G−↑. Thus, the unidirectional update method is more suitable for training ANNs consisting of CuPc/p-6P synaptic devices, because the NL of the LTP characteristic curve is much lower than that of the LTD curve. After we conducted the training process with 60,000 MNIST training data sets, we obtained the recognition rate every 5,000 training steps (1 epoch) with 10,000 test data sets, as shown in Fig. 4c and d. First, we determined the recognition rates for different pulse
current, thus negating the effect of holes originating from the p-6P layer. In contrast, in the LTD region, the NL increased from the abovementioned values up to −6 and −7.3 when the numbers of pulses were 170 and 340, respectively. Gmax/Gmin was also calculated for each pulse number, as shown in Fig. 2f. The Gmax/Gmin in region 1 (gray, # of pulses < 100) increased more sharply compared with that in region 2 (green, # of pulses > 100). In region 1, the Gmax/Gmin increased from 2.4 to 14.7 as the number of pulses increased from 16 to 70. The increase in Gmax/Gmin was relatively suppressed in region 2 (from 14.7 to 34.7) even though many more pulses were applied. This is because the ΔPSC started decreasing slightly after the 50th light pulse, resulting in a slow increase in Gmax. Furthermore, this CuPc/p-6P optoelectronic synaptic device exhibited stable synaptic characteristics even after many repeated measurements. We applied 35 light pulses and 35 voltage pulses per cycle to the device and monitored the LTP/ LTD characteristic curves for five cycles (Fig. 2g). We then obtained the mean and standard deviation of the same-level current points in the LTP/LTD characteristic curves over five cycles, and indicated these values by a circle and error bar, respectively, in Fig. 2h. The standard deviation for each value was very small and it was even comparable to the variation of each PSC value (ΔPSC). This indicates that our synaptic device has stable and consistent conductance states for weight update, regardless of its operating frequency. In addition, we obtained the NL and PSC (minimum, maximum, and intermediate) values from the LTP/ LTD characteristic curves in each cycle (Fig. 2i and j, respectively). The NL values in the LTP and LTD regions remained approximately 0.01 and 3.5 for the five cycles, respectively. The PSCmax, PSCmin, and PSCcenter values remained at approximately 49.9, 4.4, and 27.2 nA, respectively. These results indicate that this synaptic device has stable synaptic characteristics for a reliable weight update. We investigated the LTP/LTD characteristics of the synaptic device again under various light pulse conditions (Figs. S14 and S15). This is because the weight of synapses in ANNs is based on the LTP/LTD characteristic curves, which are strongly dependent on the light pulse condition. To examine the LTP characteristic, we applied light pulses of various powers (0.1, 0.4, 0.7, and 1.0 mW) with the width and wavelength of 0.5 s and 365 nm, respectively. The voltage pulse of magnitude −10 V and width 0.1 s was used for the analysis of the LTD characteristic. Fig. 3a shows the LTP/LTD characteristic curves obtained after applying 85 light pulses and 85 voltage pulses. For the power of 0.1 mW, the PSC value increased linearly from 3.3 to 17.9 nA in the LTP region and decreased nonlinearly to the initial value in the LTD region. The ΔPSC values remained constant at approximately 0.173 nA in the LTP region, but the values in the LTD region were distributed between 0.05 and 3.1 nA (Fig. S16). The stable PSC change in the LTP region indicates that the synaptic weight can be consistently updated by the light pulse. In contrast, the high ΔPSC variation in the LTD curve is predicted to cause an uneven weight update problem. When the light pulses of power 0.4 mW were applied, the PSC increased from 4.5 to 51.8 nA more rapidly, compared with the case of pulse power of 0.1 mW and then decreased to the initial value. This is because, as the power of light pulses increases, the number of holes moving to the CuPc channel increases, also increasing the number of electrons trapped at the p-6P/dielectric interface. We then obtained the NL and Gmax/Gmin values for each pulse power from the LTP/LTD characteristic curve (Fig. 3b). For the pulse powers of 0.1 and 0.4 mW, the NL values in the LTP region were close to 0 (linear increase of PSC). This is because similar numbers of holes move to the CuPc channel at every light pulse, regardless of the pulse number. As the pulse power increased to 0.7 and 1.0 mW, the NL slightly increased to 0.21 and 0.5, respectively (nonlinear increase of PSC). This can be explained by the numerous trapped electrons causing a larger increase in the channel current, compared with that by the additional holes moving from the p6P layer. In the LTD region, the NL increased in a negative direction from −4.6 to −6.1 as the pulse power increased from 0.1 to 1.0 mW. This is because more electrons are released in the first few voltage 5
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Fig. 3. LTP/LTD characteristics of the CuPc/p-6P optoelectronic synapse under various light pulse conditions: (a) power and (c) width. NL and Gmax/Gmin obtained from the LTP/LTD characteristic curves for different (b) light pulse powers and (d) light pulse widths.
Fig. 4. (a) Schematic illustration of single-layer-perceptron-based ANN with 784 input neurons and 10 output neurons fully connected through 7840 synaptic weights. (b) Representation of synaptic weight using the conductance difference between two equivalent optoelectronic synaptic devices (left) and detailed weight updating processes based on unidirectional and bidirectional update methods (right). (c) Recognition rate as a function of the number of training epochs for the cases with various light pulse conditions. (d) Recognition rate as a function of the number of training epochs for the cases where unidirectional and bidirectional update methods are used. 6
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p-6P films were deposited thermally on the SiO2 surface at a rate of 1 nm/min. During the deposition, the substrate temperature was set to 180 °C. Subsequently, the source and drain electrodes (thickness = 40 nm) were formed by evaporating gold through a shadow mask on top of the CuPc layer. The width and length of the active channel were 1000 and 100 μm, respectively. The current-sensing atomic force microscopy (CSAFM) measurement was performed using an Agilent Technologies 5500 AFM/SPM System equipped with conductive Pt–Ir coated tips (Bruker Nano Inc). The electrical properties of the optoelectronic synapse were measured using a semiconductor parameter characterization system (Keithley 4200) in ambient condition and at room temperature. A laser diode was used as the light source, which was controlled by a function/arbitrary waveform generator (Agilent 33220A) to generate the light pulse. The light power was adjusted from 0.1 to 1 mW (light power intensity from 0.13 to 1.27 mW/cm2), which was calibrated using an optical power meter (Thorlabs PM 100D). In the weight update method based on the hardware-based backpropagation algorithm, as the measured conductance states always have positive values, the synaptic weight is represented as the difference between the conductance values of two equivalent synaptic devices (W = G+ − G−). To determine whether the synaptic weight was potentiated or depressed, we calculated the sign of ΔW (sgn(ΔW)) using the difference between the output value ( ym ) and each label value (km ):
width (solid line, 1.0 mW) and power (dashed line, 0.5 s) conditions using the bidirectional update method (Fig. 4c). After learning for the 3rd epoch, the recognition rates were saturated, where the highest value appeared to be approximately 68% in the case of pulse width 1.0 s. As Gmax/Gmin decreased in the order of the corresponding pulse conditions #2→#4→#3→#1 (inset table), the recognition rate also decreased from 67% to 43%. This indicates that Gmax/Gmin had a greater effect than NL (|NLP|+|NLD|) on the recognition rate for MNIST digit patterns. Subsequently, to maximize the effect of the superior linearity in the LTP region, we applied the unidirectional update method to the training tasks for two cases of pulses i.e., condition #2 with 1.0 s and 1.0 mW (solid line) and condition #4 with 0.5 s and 1.0 mW (dashed line). As shown in Fig. 4d, the recognition rates predicted using the unidirectional update method (blue) were higher than those of the bidirectional case (red). For the pulse conditions #2 and #4, the recognition rates were 78% and 73%, respectively. This is because the LTP characteristic curve showing better linearity was only used for updating weights, thereby improving the effective NL (7.49 → 1.38 and 6.57 → 0.5). We also obtained the recognition rates for various pulse conditions, with pulse widths of 0.3, 0.5, and 1.0 s, and pulse powers of 0.4, 0.7, and 1.0 mW (Fig. S17). Similar to the above trend, the cases where the unidirectional update method was used presented higher recognition rates under all pulse conditions. Through this supervised learning simulation for MNIST digit patterns, we confirmed that the recognition rate of the ANN consisting of the proposed optoelectronic synaptic devices increased up to 78% with the use of the unidirectional update method. Through this supervised learning simulation for MNIST digit patterns, we confirmed that the recognition rate of the ANN consisting of the proposed optoelectronic synaptic devices increased up to 78% with the use of the unidirectional update method. In order to show the possibility to improve the recognition rate further, we configured a 400 × 200 × 10 double-layer ANN using the LTP/LTD characteristic curves obtained under various light pulse conditions and then performed the training/recognition tasks for the cropped MNIST pattern data (20 × 20) [36,37]. As a result, the maximum recognition rate reached up to 87.11% for the case using the pulse power of 0.4 mW (Fig. S19 and Table S1).
sgn(ΔW) > 0 if km-ym > 0 (potentiation),
(1)
sgn(ΔW) < 0 if km-ym < 0 (depression).
(2)
For potentiating the synaptic weight (sgn(ΔW) > 0), G+ was increased and G− was decreased simultaneously (W ↑ = G+ ↑ − G−↓). In contrast, for the depression (sgn(ΔW) < 0), G+ was decreased and G− was increased simultaneously (W ↓ = G+ ↓ − G−↑). When the conductance of the synaptic device reached the maximum value (Gmax ), both G+ and G− were initialized to Gmin (refresh), and the larger of the two was increased again by the difference between G+ and G− (reprogram). As mentioned in the main text, this is called the bidirectional update method, where both conductance states in the long-term potentiation (LTP) and long-term depression (LTD) characteristics curves are used for updating. The unidirectional update method is different in that it uses only the LTP characteristic. When potentiating the synaptic weight (sgn(ΔW) > 0), only G+ was increased (W ↑ = G+ ↑ − G− ), whereas for the depression of the weight (sgn(ΔW) < 0), only G− was increased (W ↓ = G+ − G−↑).
3. Conclusion In conclusion, we reported an optoelectronic synaptic device fabricated on a CuPc/p-6P heterojunction. During the operation of this synaptic device, photogenerated holes moved to the CuPc channel and photogenerated electrons were trapped at the p-6P/dielectric interface, thereby causing a consistent increase in the channel current. Basic synaptic characteristics of this synaptic device, such as PSC and LTP/LTD, were successfully demonstrated by using light and voltage pulses for potentiating and depressing the synaptic weight, respectively. Particularly, the NL in the LTP characteristic curve was less than 0.01, which was close to the ideal value for the training process of an ANN. The maximum recognition rate for MNIST digit patterns was as high as 78% through the use of the unidirectional update method with only the LTP characteristic, even though the ANN was based on a single-layer perceptron model. As the number of electron–hole pairs generated by a light pulse was constant, the numbers of holes supplied to the CuPc channel and electrons trapped at the p-6P/dielectric interface were also similar at every light pulse injection, resulting in a linear increase in the channel conductance. The proposed synaptic device is expected to play a significant role in the progress of synaptic device technology using optoelectronic spiking signals.
Acknowledgement The authors acknowledge the grants from Basic Science Research Program and Nano Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B2005790, 2016M3A7B4910426, and 2017R1A4A1015400). J.S. acknowledges support by the National Natural Science Foundation of China (61975241). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.nanoen.2019.104095. References [1] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Nature 529 (2016) 484. [2] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis, Science 362
4. Methods CuPc and p-6P were purchased from Sigma-Aldrich. A heavily doped n-type silicon substrate with a thermally grown 200-nm-thick SiO2 gate dielectric was utilized as the bottom gate. Further, 30 nm CuPc/7.5 nm 7
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Dr. Chuan Qian received his B.S. in Applied Physics from Xinjiang University in 2012 and Ph.D. degree in Physics from Central South University in 2017. Afterwards, he worked as a postdoctoral researcher at Sungkyunkwan University (SKKU) in 2018. Now, he is a postdoctoral researcher in Jeong Ho Cho's group at Yonsei University. His research interest includes artificial synaptic devices.
(2018) 1140–1144. [3] G. Indiveri, S.-C. Liu, Proc. IEEE Inst. Electr. Electron. Eng. 103 (2015) 1379–1397. [4] C.K. Machens, Science 338 (2012) 1156–1157. [5] R.A. Nawrocki, R.M. Voyles, S.E. Shaheen, IEEE Trans. Electron Devices 63 (2016) 3819–3829. [6] W. Zhang, R. Mazzarello, M. Wuttig, E. Ma, Nat. Rev. Mater. 4 (2019) 150–168. [7] S. Yu, Y. Wu, R. Jeyasingh, D. Kuzum, H.-S.P. Wong, IEEE Trans. Electron Devices 58 (2011) 2729–2737. [8] M. Prezioso, F. Merrikh-Bayat, B.D. Hoskins, G.C. Adam, K.K. Likharev, D.B. Strukov, Nature 521 (2015) 61. [9] H.-S.P. Wong, S. Raoux, S. Kim, J. Liang, J.P. Reifenberg, B. Rajendran, M. Asheghi, K.E. Goodson, Proc. IEEE Inst. Electr. Electron. Eng. 98 (2010) 2201–2227. [10] H. Wang, Q. Zhao, Z. Ni, Q. Li, H. Liu, Y. Yang, L. Wang, Y. Ran, Y. Guo, W. Hu, Y. Liu, Adv. Mater. 30 (2018) 1803961. [11] M.-K. Kim, J.-S. Lee, Nano Lett. 19 (2019) 2044–2050. [12] Y. van de Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria, S. Agarwal, M.J. Marinella, A. Alec Talin, A. Salleo, Nat. Mater. 16 (2017) 414. [13] C. Qian, J. Sun, L.-A. Kong, G. Gou, J. Yang, J. He, Y. Gao, Q. Wan, ACS Appl. Mater. Interfaces 8 (2016) 26169–26175. [14] P. Gkoupidenis, N. Schaefer, B. Garlan, G.G. Malliaras, Adv. Mater. 27 (2015) 7176–7180. [15] F. Yu, L.Q. Zhu, H. Xiao, W.T. Gao, Y.B. Guo, Adv. Funct. Mater. 28 (2018) 1804025. [16] Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh, D. Son, J. Kang, A.M. Foudeh, C. Zhu, Y. Lee, S. Niu, J. Liu, R. Pfattner, Z. Bao, T.-W. Lee, Science 360 (2018) 998–1003. [17] S. Kim, J. Yoon, H.-D. Kim, S.-J. Choi, ACS Appl. Mater. Interfaces 7 (2015) 25479–25486. [18] Y. Wang, Z. Lv, J. Chen, Z. Wang, Y. Zhou, L. Zhou, X. Chen, S.-T. Han, Adv. Mater. 30 (2018) 1802883. [19] S.J. Martin, P.D. Grimwood, R.G.M. Morris, Annu. Rev. Neurosci. 23 (2000) 649–711. [20] S. Kim, B. Choi, M. Lim, J. Yoon, J. Lee, H.-D. Kim, S.-J. Choi, ACS Nano 11 (2017) 2814–2822. [21] E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal, Y. Li, Y. Tuchman, C.D. James, M.J. Marinella, J.J. Yang, A. Salleo, A.A. Talin, Science 364 (2019) 570–574. [22] E. Pastrana, Nat. Methods 8 (2011) 24–25. [23] A.D. Douglass, S. Kraves, K. Deisseroth, A.F. Schier, F. Engert, Curr. Biol. 18 (2008) 1133–1137. [24] D. Huber, L. Petreanu, N. Ghitani, S. Ranade, T. Hromádka, Z. Mainen, K. Svoboda, Nature 451 (2008) 61–64. [25] L. Yin, C. Han, Q. Zhang, Z. Ni, S. Zhao, K. Wang, D. Li, M. Xu, H. Wu, X. Pi, D. Yang, Nano Energy 63 (2019) 103859. [26] S. Seo, S.-H. Jo, S. Kim, J. Shim, S. Oh, J.-H. Kim, K. Heo, J.-W. Choi, C. Choi, S. Oh, D. Kuzum, H.-S.P. Wong, J.-H. Park, Nat. Commun. 9 (2018) 5106. [27] Y. Lee, J.Y. Oh, W. Xu, O. Kim, T.R. Kim, J. Kang, Y. Kim, D. Son, J.B.-H. Tok, M.J. Park, Z. Bao, T.-W. Lee, Sci. Adv. 4 (2018) eaat7387. [28] J. Sun, S. Oh, Y. Choi, S. Seo, M.J. Oh, M. Lee, W.B. Lee, P.J. Yoo, J.H. Cho, J.H. Park, Adv. Funct. Mater. 28 (2018) 1804397. [29] S. Ham, S. Choi, H. Cho, S.-I. Na, G. Wang, Adv. Funct. Mater. 10 (2018) 1806646. [30] J. Jiang, W. Hu, D. Xie, J. Yang, J. He, Y. Gao, Q. Wan, Nanoscale 11 (2019) 1360–1369. [31] Y. Chen, W. Qiu, X. Wang, W. Liu, J. Wang, G. Dai, Y. Yuan, Y. Gao, J. Sun, Nano Energy 62 (2019) 393–400. [32] C. Qian, J. Sun, L. Zhang, H. Huang, J. Yang, Y. Gao, J. Phys. Chem. C 119 (2015) 14965–14971. [33] K.M. Lau, J.X. Tang, H.Y. Sun, C.S. Lee, S.T. Lee, D. Yan, Appl. Phys. Lett. 88 (2006) 173513. [34] N. Koch, E. Zojer, A. Rajagopal, J. Ghjisen, R.L. Johnson, G. Leising, J.-J. Pireaux, Adv. Funct. Mater. 11 (2001) 51–58. [35] A. Novikov, Solid State Electron. 54 (2010) 8–13. [36] P. Chen, X. Peng, S. Yu, IEEE International Electron Devices Meeting (IEDM), (2017), pp. 6.1.1–6.1.4. [37] P. Chen, X. Peng, S. Yu, IEEE Trans. CAD 37 (2018) 3067–3080.
Seyong Oh received his BS in the Department of Electronic and Electrical Engineering from SKKU in 2015. Now he is Ph.D. candidate in Jin-Hong Park's group at Department of Electrical and Computer Engineering from SKKU. His research interest includes neuromorphic devices and circuits.
Prof. Jin-Hong Park has been an associate professor with School of Electronic and Electrical Engineering and SKKU Advanced Institute of Nano Technology in SKKU since 2011. From 2009 to 2011, he had been Kyung Hee University and IBM Thomas J. Watson Research Center in Yorktown Heights, NY. He received M.S./Ph.D. degree in Electrical Engineering from Stanford University in 2006 and 2009, and B.S. degree in Electrical Engineering from SKKU in 2004. His current research focuses on (1) next generation low-power devices based on the 2D vdW materials (multi-valued logic devices and neuromorphic devices) and (2) 2D vdW material-based process technologies/devices.
Prof. Jeong Ho Cho obtained his B.S. in chemical engineering from Sogang University in 2001 and his MS and Ph.D. in Chemical Engineering from POSTECH in 2006. He was a postdoctoral researcher in Department of Chemical Engineering and Materials Science at University of Minnesota (2006–2008) and then joined as a faculty at Soongsil University (2008–2012) and Sunkyunkwan University (2012–2018). He now is a professor at Yonsei University with an appointment in Department of Chemical and Biomolecular Engineering. His research interests include organic electronic devices (transistor, memory, and sensor) and 2-dimensional nanomaterials.
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