Letter Cite This: ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
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Coplanar Multigate MoS2 Electric-Double-Layer Transistors for Neuromorphic Visual Recognition Dingdong Xie,† Jie Jiang,*,† Wennan Hu,† Yongli He,‡ Junliang Yang,† Jun He,† Yongli Gao,†,§ and Qing Wan*,‡
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†
Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China ‡ School of Electronic Science & Engineering and Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, Nanjing 210093, China § Department of Physics and Astronomy, University of Rochester, Rochester, New York 14627, United States S Supporting Information *
ABSTRACT: Spatial coordinate and visual orientation recognition in cortical cells play important roles in the visual system. Herein, spatiotemporally processed visual neurons are mimicked by a facile coplanar multigate two-dimensional (2D) MoS2 electric-double-layer transistor with proton-conducting poly(vinyl alcohol) electrolytes as laterally coupled gate dielectrics. Fundamental neuromorphic behaviors, e.g., excitatory postsynaptic current and paired-pulse facilitation, were successfully mimicked. For the first time, a proof-of-principle artificial visual neural network system for mimicking spatiotemporal coordinate and orientation recognition was experimentally demonstrated in such devices. The experimental results provide a promising opportunity for adding intelligent spatiotemporally-processed functions in emerging brain-like neuromorphic nanoelectronics. KEYWORDS: neuromorphic devices, electric-double-layer transistors, two-dimensional MoS2, spatiotemporal coordinate, visual orientation recognition
considered as one of the most promising 2D materials for replacing widely studied 2D graphene because of its semiconductivity (the bandgap could change from 1.2 eV in the bulk to 1.8 eV in monolayers).11−14 Therefore, neuromorphic devices based on 2D materials with transistor configuration have attracted considerable attention for future intelligent electronic systems.10−13 For example, Hersam et al. demonstrated that a 2D three-terminal MoS2 transistor could act as a hybrid of memristor and transistor, which could mimic a biological synapse between neurons in a scalable fabrication process.12 Arnold et al. also mimicked the synapse element through the interface trapping/detrapping effect in MoS2 transistors. They argued that the trapping/detrapping mechanism depended on the electron transfer between the interface of MoS2 channel and ambient water.13 Even though some interesting synaptic functions have successfully been mimicked
Neurons with thousands of synapses are often thought to be the computing engines of the human brain.1−3 Each synapse memorizes a weight to compute the connection strength between these neurons, and the synaptic strength (weight) can be precisely regulated by various ion concentrations, such as Ca2+, Na+, and K+,2,3 which is referred to as synapse plasticity. Therefore, a hardware implementation of synapses/neurons by solid-state devices is of great significance for realizing brain-like recognition and computing systems. To date, synaptic devices based on only two-terminal memristors were widely investigated;4,5 however, three-terminal devices, e.g., nickelate-based transistors, carbon nanotube transistors, and oxide-based transistors, were also creatively proposed for artificial synapses and neuromorphic systems.6−8 Recently, two-dimensional (2D) materials, with a single or few layers of crystal unit cells have become a fertile ground for exploring unique physical/chemical properties and interesting applications because their atomic-level thin bodies allow for aggressive scaling and excellent electrostatic control.9−14 In particular, in comparison zero-bandgap graphene, MoS2 has recently been © XXXX American Chemical Society
Received: May 3, 2018 Accepted: July 24, 2018 Published: July 24, 2018 A
DOI: 10.1021/acsami.8b07234 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
Letter
ACS Applied Materials & Interfaces in these devices, the fundamental and important features of the human brain, including spatial coordinate and visual orientation recognition, are yet to be explored comprehensively. Spatial coordinate and visual orientation recognition in cortical cells,15 a well-known paradigm of spatiotemporal processing, were extensively studied in a visual neural network system, which is of great importance for neuromorphic visual applications.15−20 The spatial information-processing function has been demonstrated using artificial synaptic devices or arrays of devices, such as memristive grids,17 and floating-gate transistor arrays.18 Recently, Malliaras et al. studied a spatially related neuromorphic function of the visual cortex using an organic electrochemical transistor.19 More recently, Shen et al. demonstrated an artificial flexible visual memory system based on memristor arrays.20 However, to date, no attention has been paid to emulating such advanced visual spatiotemporal processing using emerging 2D neuromorphic devices. In this work, 2D MoS2 electric-double-layer (EDL) transistors with coplanar multigate input arrays were fabricated using poly(vinyl alcohol) (PVA) proton-conducting electrolyte as the coupling dielectric. For the first time, a proof-of-principle artificial 2D visual neural-network system for mimicking spatiotemporal coordinate and orientation recognition was experimentally demonstrated. The experimental finding results would provide a new approach for introducing intelligent visual recognition functions in emerging neuromorphic electronics. The 2D MoS2 transistors employed herein were fabricated via the photolithography, thermal-evaporation, and lift-off processes (more details are provided in Supporting Information 1). Figure 1a shows a magnified schematic of two adjacent neurons with a synapse. Whenever a stimulus triggers an action potential, neurotransmitters are released from the presynaptic neuron into the synaptic cleft.1−3 These neurotransmitters can diffuse across the synaptic cleft and subsequently bind to the receptors in the post-neuron, eventually triggering a subsequent action potential in the post-neuron. Such a biological synapse could be mimicked using a proton-conducting electrolyte-coupled 2D MoS2 transistor, the schematic of which is shown in Figure 1b. The thickness of the MoS2 flake was measured via atomic force microscopy (AFM), as shown in Figure 1c, and was estimated to be ∼8 nm, which corresponded to ∼12 layers on the basis of 0.65 nm thickness per layer value.21 The inset of Figure 1c is a schematic of the layered atomic structure of the MoS2 flake comprising a stack of layers separated by the van der Waals forces. Moreover, frequency-dependent capacitance (Ci) measurement of the PVA membrane is shown in Figure 1d, with frequencies varying from 1.0 Hz to 100 kHz. Ci was measured in a sandwiched metal/PVA/metal structure, as shown in the inset of Figure 1d. It is clearly evident that the capacitance curve could be divided into three domains: (1) when f > 8 kHz, the capacitance showed a constant value of ∼1.0 nF/cm2, which represents a normal capacitive behavior due to the dipolar relaxation of PVA polymer dielectric; (2) when 60 Hz< f < 8 kHz, the capacitance exhibited a strong frequency-dependent characteristic, which represents a resistive behavior due to the ionic relaxation phenomenon in PVA-based electrolyte; and (3) when f < 60 Hz, the capacitance displayed a weak frequency-dependent characteristic with a large capacitance value of 4.0−9.0 μF/cm2, due to the mobile proton-induced EDL effect in the PVA-based electrolyte.22 Furthermore, the capacitance at a low frequency in PVA reached the maximum value, i.e., ∼9.0 μF/cm2, at 1.0 Hz. Such a strong frequency-
Figure 1. (a) Magnified schematic diagram for two adjacent neurons with a synapse. (b) Schematic image of the PVA coupled 2D MoS2 transistor. (c) AFM image at the MoS2 flake edge. Inset: schematic diagram of layered atomic structure of multilayer MoS2. (d) Specific capacitance of the PVA electrolyte film as a function of frequency. Inset: sandwiched metal/PVA/metal structure for the capacitance measurement. (e) EPSC triggered by a presynaptic spike (1.5 V, 10 ms). (f) Spike duration-dependent EPSC where the spike amplitude is 1.5 V.
dependent capacitance can be explained through the EDL theory owing to the existence of moving protons in PVA.11,22 Hence, these results clearly indicated that the PVA film could serve as an excellent gate dielectric for EDL electrostatic modulation and a new concept in electronic applications. Furthermore, detailed discussion about the characteristics of the 2D MoS2 transistor with a PVA-based electrolyte can be found in Supporting Information 2. In the proposed 2D MoS2 transistor, the coplanar gate with a PVA-based electrolyte could serve as a presynaptic terminal, while the ultrathin MoS2 channel layer with source/drain electrodes was considered as a postsynaptic terminal. Ions from the PVA dielectric played the same function as ion flux in neurotransmitters, whereas the channel conductance could be regarded as the synaptic weight.11 These mobile ions could migrate along the external electric field and exhibit a relaxation phenomenon. Thus, they could potentially be good neurotransmitters between the presynaptic terminal (gate) and the postsynaptic terminal (MoS2). It is an ideal choice for an efficient gate dielectric in a MoS2 transistor for neuromorphic applications. A presynaptic spike (1.5 V, 10 ms) applied to the coplanar gate terminal with a reading voltage of VDS = 0.1 V between the source and drain terminals triggered a corresponding excitatory postsynaptic current (EPSC) response in the MoS2 channel layer, as shown in Figure 1e. The resulting EPSC reached the maximum value, i.e., ∼2.1 μA, at the end of the input spike and rapidly decayed back to the resting current (∼0.7 μA), which resembled a B
DOI: 10.1021/acsami.8b07234 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
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ACS Applied Materials & Interfaces biological excitatory synapse.2,3 When a positive spike was applied to the gate terminal, protons were driven laterally and accumulated at the interface of the PVA electrolyte/MoS2 channel layer. The channel current would increase because of the increase in the number of electrons as a result of the electrostatic coupling effect.22 When the presynaptic spike is ended, protons would gradually return to their equilibrium positions, and thus the final channel current would decrease back to the quiescent current. Furthermore, as shown in Figure S4, we further examined how the sizes of gate electrodes influence the EPSC response. The results could be summarized in Table S1. From this table, it is clearly observed that a larger gate area would result in a larger EPSC when these two gate electrodes have the same gate-to-channel distance. To further investigate the temporal response, we studied the spikes in the duration-dependent EPSC (spike amplitude: 1.5 V), as shown in Figure 1f. The resulting EPSC peak values increased from ∼1.4 to ∼3.2 μA, with spike duration varying from 10 to 600 ms. Interestingly, we found that the EPSC amplitude increased almost linearly with duration up to 100 ms, but it plateaued when duration was above ∼200 ms. This is because, in the beginning, an increasing number of protons migrated to the interface of the MoS2 channel and augmented the channel conductivity of the devices employed herein;11−13 however, there was only a finite number of activated protons in the PVA film, and thus, the EPSC amplitude would finally become saturated. Moreover, the saturation time of different gate-to-channel distance and proton migration EPSC decay behavior have been discussed in Supporting Information 3. In the biological system, paired-pulse facilitation (PPF) is a common phenomenon in which the spike-induced EPSC becomes larger when the second spike follows the previous one closely,8 it is an ideal example of short-term synaptic plasticity for decoding temporal information in a visual or auditory signal.11 Such PPF function and STDP behavior could also be emulated by the proposed MoS2 transistor, further discussed in Supporting Information 4. In the visual system, the optical stimuli are first captured by the retina and then transmitted as optical information to a group of lateral geniculate nucleus (LGN) cells of the thalamus via optical nerves.15,16 Thereafter, the information from the LGN cells is subsequently transmitted to the coordinate recognition visual cortex cells in a purely electrical and/or chemical form.16 In this case, the activity of a group of LGN cells (referred to as a receptive field) is projected to a visual cortex cell and transformed into a coordinate recognition firing activity.15,16 Figure 2a shows a simplified schematic of the visual system wherein a clear three-layered abstract feedforward mode is exhibited. Retina, the first layer, is responsible for receiving inputs. Thalamus, the second layer, is where oneto-one mapping between retina and LGN cells of the thalamus exists. Visual cortex, the third layer, is where many-to-one mapping is established from the second-layer cells to the thirdlayer cells.17 Figure 2b shows a schematic of 2D MoS2 transistor with a grid of 3 × 3 coplanar gate (Gx, y) arrays. Herein, the spiking voltages with the same amplitude were applied to different coplanar gates as their different coordinate information, and the resulting EPSC amplitude was recorded as the output response. Analogous to the visual system, the coplanar multigate input array in the proposed MoS2 transistor was regarded as the receptive field of a visual cortex cell, whereas the EPSC amplitude was measured as an activity of the spatial cortical cell. When the spiking voltage with the same
Figure 2. (a) Simplified schematic of the visual system. (b) Schematic picture of the 2D MoS2 neuromorphic transistor with a grid of 3 × 3 coplanar-gate arrays. (c) Maximum EPSC measured at the coordinate of G1,1. (d) Minimum EPSC measured at the coordinate of G−1,‑1. (e) 2D EPSCs surface summarized as a function of spatial coordinate. (f) Spatial EPSCs mapping of the MoS2 neuromorphic device with the different coplanar gate coordinates.
amplitude (1.5 V, 10 ms) was applied to different coplanar gate coordinates (x, y), different EPSC amplitudes could be obtained due to the various gate-channel coupling paths in the proposed MoS2 transistor. This finding is very promising for position-sensitive recognition (PSR) in the visual system.23,24 Here, two typical EPSC responses triggered by different coplanar gate coordinates, (1, 1) and (−1, −1), are presented in Figure 2c, d, respectively. A large EPSC amplitude of 3.0 μA was set for coplanar gate coordinates: (1, 1), whereas a small EPSC amplitude of 0.2 μA was set for coplanar gate coordinates: (−1, −1). A 2D EPSC surface of the MoS2 transistor with different coplanar-gate coordinates (a 3 × 3 array) is summarized in Figure 2e. It is observed that the maximum EPSC was obtained at coplanar gate coordinates (1, 1), where the gate was the closest to the drain electrode, while the minimum EPSC was located at coplanar-gate coordinates (−1, −1), where the gate was the farthest from the drain electrode. This phenomenon was mainly attributed to the decrease in the electrolyte resistance as the distance between the gate and drain was smaller, which led to an enhancement of the transient current of the channel for smaller gate-drain distances.20 However, in the proposed device, the EPSC responses did not precisely follow this trend; this might be due to the formation of a more complex material interface in the proposed coplanar multigate 2D MoS2 device, e.g., (a) an electrochemical/electrostatic reaction between PVA and layered 2D MoS2 and (b) a Schottky barrier between the electrode and 2D MoS2. To further investigate this PSR behavior, Figure 2f shows spatial EPSC mapping of the MoS2 neuromorphic device with different coplanar gate coordinates C
DOI: 10.1021/acsami.8b07234 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
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ACS Applied Materials & Interfaces in a 3 × 3 array. From this figure, it is very interesting to mention that an apparent “sawtooth” shape was observed in the evolution from the low EPSC amplitude to the high EPSC amplitude. As a result, the EPSC amplitude strongly depended on the spatial coordinates of the coplanar gates, indicating that such a MoS2 neuromorphic device could effectively recognize position-sensitive information in a visual system. Furthermore, one of the most fundamental functions of the visual cortex is recognizing the orientations of different visual scenes.17,23 In neurobiology, orientation recognition is achieved due to the geometric alignment of the receptive fields of the LGN cells that are mapped to a simple cell in the cortex.15,16 Figure 3a shows a simplified schematic of
from one coplanar gate (G0,0) to others (G1,0, G0,1, G−1,0, and G0,−1), respectively. As shown in Figure 3c, a typical EPSC was induced by two presynaptic inputs (1.5 V, 10 ms) at G0,0 and G−1,‑1 (orientation: 225°), respectively. For this orientationdependent logic, “Xf 2” spike logic was observed because the EPSC amplitude depended on the presynaptic input from only G0,0. Furthermore, spike logics with all orientation information can be found in Figure S9. Roughly, the EPSC responses for the orientations of 45, 90, 135, and 180°, were stronger than those for the other orientations. The difference between those EPSC responses and the EPSC response in the central position was also small, which would result in an “OR” logic. However, for the rests, the device would prefer the “Xf1” and “Xf 2” logics, because one of the EPSC responses was dominated during the double-input logic integration. Herein, Figure 3d shows a summarized polar diagram of the spike logics with different orientations. Interestingly, it exhibited three different kinds of spike logics (“OR,” “Xf1,” and “Xf 2”) for all orientations. The EPSC sum (i.e., Isum) triggered by two synchronous spiking voltages (1.5 V, 10 ms) of presynaptic terminals can be further summarized in Figure 3e. It was found that the maximum EPSC amplitude corresponded to the visual orientation at 0°, whereas the minimum EPSC amplitude existed at the orientation of 225°. At the same time, if a ratio is defined for the EPSC amplitudes between the measured sum (Isum; both presynaptic spikes were in the high-voltage level) and the expected arithmetic sum (I1+I2; only single presynaptic spike was in the high-voltage level), it can be plotted as a function of presynaptic orientations as shown in Figure 3f. In the nervous system, dendritic integration plays a critical role in information transmission and computation. Nonlinear dendrites can greatly approximate an optimal response.25 The locally generated dendritic spikes offer dendrites strong superlinearities, which is the key mechanism of dendritic integration within branches.26 Interestingly, superlinear dendrite integration behaviors (Isum/ (I1+I2)>1) were observed when the presynaptic orientations were equivalent to 0, 225, 270, and 315°, whereas sublinear dendrite integration behaviors were achieved for the presynaptic orientations of 45, 90, and 180°. Note that a linear dendrite integration behavior was also observed when the presynaptic orientation was 135°. These results indicated that both nonlinear and linear dendrite integration behaviors could be mimicked by varying the presynaptic spatial orientations. More importantly, both sublinear and superlinear dendrite integrations could be realized in the nonlinear behaviors of the proposed coplanar multigate MoS2 transistor by modulating different presynaptic orientations; this finding is of great importance for visual neuromorphic electronics applications.23,24 Biologically, an individual neuron with branched dendrites can quickly perform arithmetic operations on the signals it receives from its presynaptic input terminals, thereby allowing for information to be collected and integrated before it is transformed into neural output.26,27 These inputs exist in two types: driving inputs (which can be regarded as transmitters of receptive field properties) and modulatory inputs (which can be regarded as changing the probability of some aspects of a given transmission).28 Herein, the visual neuronal arithmetic function was successfully implemented in the proposed coplanar multigate MoS2 transistor. For this device, the spikes (1.5 V, 10 ms) were applied at G0,0 and Gi,j as the driving inputs and the different visual orientations (defined based on a rule same as that shown in Figure 3b) could be used as
Figure 3. (a) Simplified schematic of orientation recognition in the visual system. (b) Schematic image of the 2D MoS2 neuromorphic device with a multigate arrays for defining different orientations. (c) Spiking logic response of EPSC triggered by two presynaptic driving inputs of G0,0 and G−1,‑1. (d) Summarized polar diagram of the spike logics with different orientations. (e) Polar diagram of EPSCs measured by two synchronous spiking voltages of G0,0 and Gi,j. (f) Polar diagram for the ratio of EPSC between the measured sum and the expected sum.
orientation recognition in a visual system.24 There also exists a many-to-one mapping between the thalamus and visual cortex layers, where many on/off center cells of LGN innervate at an individual cortical cell to form its receptive field.17 Orientation recognition can be regarded as a computational feature in the cortex, starting at the first stage of thalamocortical interaction.24 Here, such a receptive field of a simple cell could be mimicked by the coplanar multigate MoS2 transistor for the orientation recognition of a visual system, as schematically shown in Figure 3b. From this diagram, the visual orientation was mimicked by the directions between two different coplanar gates. For example, the orientations of 0, 90, 180, and 270° can be simply defined D
DOI: 10.1021/acsami.8b07234 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
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ACS Applied Materials & Interfaces
modulatory visual orientations could effectively alter the type of the neuronal I−O relationship. Finally, from this I−O relationship, a good linear fitting was obtained (solid line in Figure 4c). This fitting to the I−O relationship can be implemented using the linear function: y = kx + b, where k and −b/k are the slope and x-axis intercept of the linear fitting, respectively. Figure 4d shows a polar diagram of the slope of the linear fitting for different spatial visual orientations. The slope strongly depended on different spatial orientations, and the maximum value was found to be 23.4 (nA s)/spikes when the spatial visual orientation was 225°. In neuroscience, a slope change represents a mathematical multiplicative operation (i.e., neuronal gain modulation), whereas an x-axis intercept change represents an additive (or subtractive) operation.27,29 Thus, the results presented herein revealed that neuronal gain modulation was successfully mimicked through the control of modulatory presynaptic inputs (i.e., the analogy of different spatial orientations in a visual system) in the proposed coplanar multigate MoS2 transistor. It is well-known that gain modulation is a key mechanism whereby cortical neurons cooperate with process information because it is fundamental to coordinate transformations for object perception and all kinds of spatial processing.29,30 Moreover, Figure 4e shows a polar diagram of the extracted x-axis intercept in linear fitting for different spatial visual orientations. From this figure, we can see that the x-axis intercept was positive (x0 = 1.4 spikes/s) when the spatial visual orientation was 180°, representing neuronal subtractive excitation behavior.27 However, for the remaining spatial visual orientations, all x-axis intercepts were negative, revealing neuronal additive inhibition behavior as a counterpart.27,30 In visual neuroscience, both the nonlinear multiplicative algorithm and linear additive algorithm cooperate closely and they process the important biological information together for visual orientation recognition.27−30 Therefore, a multigate MoS2 transistor with strong spatial orientation selectivity is enormously beneficial to the development of more advanced intelligent computing and complex orientation recognition for visual cortex cells. This finding plays a critical role in processing different types of spatial information in the human brain. Herein, coplanar multigate MoS2 transistors laterally coupled with PVA electrolytes were successfully fabricated for neuromorphic device applications. Neuromorphic functions, such as excitatory postsynaptic current and paired-pulse facilitation were successfully mimicked. Most importantly, with multi-in-plane gates as the modulatory input terminals, spatiotemporal coordinate and visual orientation recognition, features of the visual cortex cells of the human brain, were also demonstrated experimentally. Such a coplanar multigate singledevice design could provide a promising approach to alleviate the difficulties of the current system design for artificial visual recognition. At present, in 2D nanoelectronics, it is of great importance to explore an intelligent artificial visual system that can be integrated into optical sensors. Although it is a preliminary experiment in 2D neuromorphic electronics, the proposed concept may have enormous benefits in numerous applications, such as intelligent electronic eyes, multifunctional robotics, and auxiliary equipment for visually disabled people.
modulatory input signals. Therefore, the resulting EPSC response was defined as the corresponding neuronal output. Such a fundamental neural computation was based on the transformation of incoming synaptic signals into specific output patterns, established by the neural input−output (I− O) relationship.29,30 In particular, the way in which information was encoded was crucial for understanding this neuronal I−O relationship. Generally, neuronal coding is divided into two extremes: rate coding and temporally correlated coding.27 Figure 4a illustrates the rate coding plan
Figure 4. (a) Schematic diagram of the rate coding. (b) Typical EPSC responses with two different driving spike inputs. (c) Neuronal I−O relationships modulated by different orientations with rate coding scheme. (d, e) Polar diagram of the (d) slope and (e) x-axis intercept of the linear fitting for different orientations.
wherein the information was encoded based on the rate ( f = f1 + f 2) of driving inputs. Therefore, the neuronal response could be expressed as a function of F (f, d), where f and d are the rate-coding driving inputs and the visual orientation of modulatory inputs, respectively. Figure 4b shows typical EPSC responses (f = 100 spikes/s) as functions of the modulatory inputs with different visual orientations (45, 180, and 315°). From this figure, the mean corresponding EPSC amplitude (A), defined as a neuronal output parameter, could be extracted as 1.6, 1.4, and 1.8 μA, respectively. Furthermore, Figure S10 shows the EPSC amplitudes of neuronal outputs with different visual orientations for the rate coding of driving inputs. From this figure, the resulting A in the rate coding scheme was extracted for different spatial orientation inputs. Figure 4c further summarizes the output A as a function of the spike input rate ranging from 10 to 100 spikes/s for each orientation. Interestingly, the resulting A in the neuronal I−O relationship decreased with the spike input rate in a good linear manner. More importantly, it was clearly observed that different spatial visual orientations would lead to distinct output A, indicating that changing the information on E
DOI: 10.1021/acsami.8b07234 ACS Appl. Mater. Interfaces XXXX, XXX, XXX−XXX
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(10) Tian, H.; Mi, W.; Wang, X. F.; Zhao, H.; Xie, Y. X.; Li, C.; Li, Y. X.; Yang, Y.; Ren, T. L. Graphene Dynamic Synapse with Modulatable Plasticity. Nano Lett. 2015, 15, 8013−8019. (11) Jiang, J.; Guo, J.; Wan, X.; Yang, Y.; Xie, H.; Niu, D.; Yang, J.; He, J.; Wan, Q. 2D MoS2 Neuromorphic Devices for Brain-Like Computational Systems. Small 2017, 13, 1700933. (12) Sangwan, V. K.; Lee, H. S.; Bergeron, H.; Balla, I.; Beck, M. E.; Chen, K. S.; Hersam, M. C. Multi-Terminal Memtransistors from Polycrystalline Monolayer Molybdenum Disulfide. Nature 2018, 554, 500−504. (13) Arnold, A. J.; Razavieh, A.; Nasr, J. R.; Schulman, D. S.; Eichfeld, C. M.; Das, S. Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS2 Transistors. ACS Nano 2017, 11, 3110−3118. (14) Jiang, J.; Zheng, Z.; Guo, J. Tuning the Hysteresis Voltage in 2D Multilayer MoS2 FETs. Phys. B 2016, 498, 76−81. (15) Buonomano, D. V.; Maass, W. State-Dependent Computations: Spatiotemporal Processing in Cortical Networks. Nat. Rev. Neurosci. 2009, 10, 113−125. (16) Hubel, D. H.; Wiesel, T. N. Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex. J. Physiol. 1962, 160, 106−154. (17) Yu, S.; Gao, B.; Fang, Z.; Yu, H.; 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. (18) Gupta, P.; Markan, C. M. An Adaptable Neuromorphic Model of Orientation Selectivity Based on Floating Gate Dynamics. Front. Neurosci. 2014, 8, 54. (19) Gkoupidenis, P.; Koutsouras, D. A.; Lonjaret, T.; Fairfield, J. A.; Malliaras, G. G. Orientation Selectivity in a Multi-Gated Organic Electrochemical Transistor. Sci. Rep. 2016, 6, 27007. (20) Chen, S.; Lou, Z.; Chen, D.; Shen, G. An Artificial Flexible Visual Memory System Based on an UV-Motivated Memristor. Adv. Mater. 2018, 30, 1705400. (21) Radisavljevic, B.; Radenovic, A.; Brivio, J.; Giacometti, I. V.; Kis, A. Single-Layer MoS2 Transistors. Nat. Nanotechnol. 2011, 6, 147−150. (22) Hu, W. N.; Zheng, Z. M.; Jiang, J. Vertical Organic-Inorganic Hybrid Transparent Oxide TFTs Gated by Biodegradable ElectricDouble-Layer Biopolymer. Org. Electron. 2017, 44, 1−5. (23) Ursino, M.; La Cara, G. E. A Model of Contextual Interactions and Contour Detection in Primary Visual Cortex. Neural. Netw. 2004, 17, 719−735. (24) Somers, D. C.; Nelson, S. B.; Sur, M. An Emergent Model of Orientation Selectivity in Cat Visual Cortical Simple Cells. J. Neurosci. 1995, 15, 5448−5465. (25) Spruston, N.; Kath, W. L. Dendritic Arithmetic. Nat. Neurosci. 2004, 7, 567−569. (26) Poirazi, P.; Brannon, T.; Mel, B. W. Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell. Neuron 2003, 37, 977−987. (27) Silver, R. A. Neuronal Arithmetic. Nat. Rev. Neurosci. 2010, 11, 474−489. (28) Sherman, S. M.; Guillery, R. W. On the Actions that One Nerve Cell Can Have on Another: Distinguishing “Drivers” From “Modulators. Proc. Natl. Acad. Sci. U. S. A. 1998, 95, 7121−7126. (29) Salinas, E.; Thier, P. Gain Modulation. Neuron 2000, 27, 15− 21. (30) Mitchell, S. J.; Silver, R. A. Shunting Inhibition Modulates Neuronal Gain During Synaptic Excitation. Neuron 2003, 38, 433− 445.
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsami.8b07234. Experiments and Methods; supplementary text, Figures
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S1−S10, and Table S1 (PDF)
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. *E-mail:
[email protected]. ORCID
Junliang Yang: 0000-0002-5553-0186 Qing Wan: 0000-0003-1781-2307 Author Contributions
J.J. and Q.W. proposed and supervised the project. D.D.X. and W.N.H. coperformed the device fabrication. J.J. D.D.X., W.N.H., and Y.L.H. coperformed the electrical measurements. Y.J.L., J.H., and Y.L.G contributed to fabricating and characterizing the MoS2 transistors. All authors discussed the results and commented on the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant 61404176, 11674162, 11334014), and the Project funded by China Postdoctoral Science Foundation (Grant 2018M632985, 2018T110839), and Natural Science Foundation of Hunan province (Grants 2018JJ3652), and National Science Foundation for Distinguished Young Scholars of China (Grant 61425020), and the Fundamental Research Funds for the Central Universities of Central South University (Grant 2017zzts701).
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