SCIENTIA SINICA Informationis, Volume 50 , Issue 6 : 892-912(2020) https://doi.org/10.1360/SSI-2019-0248

Recent progress on optoelectronic synaptic devices

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  • ReceivedNov 18, 2019
  • AcceptedDec 19, 2019
  • PublishedMay 25, 2020


Neuromorphic (brain-like) computing has great potential to solve the von Neumann bottleneck due to its self-adaptive learning, high-parallel computing capability, and low-power consumption. Realization of neuromorphic computing depends on the development of synaptic devices that mimic biological synapses. Initially, synaptic devices were electronic, and such devices face significant challenges relative to optimization of bandwidth, connectivity, and density. It has been recently shown that the incorporation of light to make optoelectronic synaptic devices brings new opportunities for the development of synaptic devices. On one hand, light enables high bandwidth, low crosstalk, low energy consumption, and no delay. On the other hand, optoelectronic devices can be used to simulate special neurobehavioral functions, such as vision. As the basis of optoelectronic integrated neural networks, optoelectronic synaptic devices are expected to greatly contribute to the development of high-performance and low-power neuromorphic computing. In this review we introduce the basic properties of optoelectronic synaptic devices. Different types and applications that have been reported for optoelectronic devices are discussed. In addition, future prospects for the development of optoelectronic synaptic devices is outlined.

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[1] von NEUMANN J. The Principles of Large-Scale Computing Machines. IEEE Ann Hist Comput, 1988, 10: 243-256 CrossRef Google Scholar

[2] Waldrop M M. The chips are down for Moore's law.. Nature, 2016, 530: 144-147 CrossRef PubMed ADS Google Scholar

[3] Merolla P A, Arthur J V, Alvarez-Icaza R. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668-673 CrossRef PubMed ADS Google Scholar

[4] Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1: 22-29 CrossRef Google Scholar

[5] Upadhyay N K, Joshi S, Yang J J. Synaptic electronics and neuromorphic computing. Sci China Inf Sci, 2016, 59: 061404 CrossRef Google Scholar

[6] Attwell D, Laughlin S B. An energy budget for signaling in the grey matter of the brain.. J Cereb Blood Flow Metab, 2001, 21: 1133-1145 CrossRef PubMed Google Scholar

[7] Drachman D A. Do we have brain to spare. Neurology, 2005, 64: 2004-2005. Google Scholar

[8] Indiveri G, Liu S C. Memory and Information Processing in Neuromorphic Systems. Proc IEEE, 2015, 103: 1379-1397 CrossRef Google Scholar

[9] Markram H. The blue brain project.. Nat Rev Neurosci, 2006, 7: 153-160 CrossRef PubMed Google Scholar

[10] Machens C K. Building the Human Brain. Science, 2012, 338: 1156-1157 CrossRef PubMed ADS Google Scholar

[11] Prezioso M, Merrikh-Bayat F, Hoskins B D. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61-64 CrossRef PubMed ADS arXiv Google Scholar

[12] Kuzum D, Yu S, Wong H-S P. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24: 382001. Google Scholar

[13] Esser S K, Merolla P A, Arthur J V. Convolutional networks for fast, energy-efficient neuromorphic computing.. Proc Natl Acad Sci USA, 2016, 113: 11441-11446 CrossRef PubMed Google Scholar

[14] Cheng Z, Ríos C, Pernice W H P. On-chip photonic synapse. Sci Adv, 2017, 3: e1700160 CrossRef PubMed ADS Google Scholar

[15] Ananthanarayanan R, Esser S K, Simon H D, et al. The cat is out of the bag: cortical simulations with 10$^9$ neurons, 10$^{13}$ synapses. In: Proceedings of the IEEE Conference on High Performance Computing Networking, Storage and Analysis, Portland, 2009. 1-12. Google Scholar

[16] Yang R, Terabe K, Yao Y. Synaptic plasticity and memory functions achieved in a WO$_{3-x}$-based nanoionics device by using the principle of atomic switch operation. Nanotechnology, 2013, 24: 384003 CrossRef PubMed ADS Google Scholar

[17] Kim K, Chen C L, Truong Q. A carbon nanotube synapse with dynamic logic and learning.. Adv Mater, 2013, 25: 1693-1698 CrossRef PubMed Google Scholar

[18] Shi J, Ha S D, Zhou Y. A correlated nickelate synaptic transistor. Nat Commun, 2013, 4: 2676 CrossRef PubMed ADS Google Scholar

[19] Zhu L Q, Wan C J, Guo L Q. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat Commun, 2014, 5: 3158-3165 CrossRef PubMed ADS arXiv Google Scholar

[20] van de Burgt Y, Lubberman E, Fuller E J. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat Mater, 2017, 16: 414-418 CrossRef PubMed ADS Google Scholar

[21] Jo S H, Chang T, Ebong I. Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett, 2010, 10: 1297-1301 CrossRef PubMed ADS Google Scholar

[22] Chang T, Jo S H, Lu W. Short-term memory to long-term memory transition in a nanoscale memristor.. ACS Nano, 2011, 5: 7669-7676 CrossRef PubMed Google Scholar

[23] Pickett M D, Medeiros-Ribeiro G, Williams R S. A scalable neuristor built with Mott memristors. Nat Mater, 2013, 12: 114-117 CrossRef PubMed ADS Google Scholar

[24] Yang R, Terabe K, Liu G. On-demand nanodevice with electrical and neuromorphic multifunction realized by local ion migration.. ACS Nano, 2012, 6: 9515-9521 CrossRef PubMed Google Scholar

[25] Ohno T, Hasegawa T, Tsuruoka T. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10: 591-595 CrossRef PubMed ADS Google Scholar

[26] Pan F, Gao S, Chen C, et al. Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater Sci Eng R, 2014, 83: 1-59. Google Scholar

[27] Wang Z, Joshi S, Savel'ev S E. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101-108 CrossRef PubMed ADS Google Scholar

[28] Benner A F, Ignatowski M, Kash J A. Exploitation of optical interconnects in future server architectures. IBM J Res Dev, 2005, 49: 755-775 CrossRef Google Scholar

[29] Zhuge X, Wang J, Zhuge F. Photonic synapses for ultrahigh-speed neuromorphic computing. Phys Status Solidi RRL, 2019, 13: 1900082. Google Scholar

[30] Zhu X, Lu W D. Optogenetics-Inspired Tunable Synaptic Functions in Memristors. ACS Nano, 2018, 12: 1242-1249 CrossRef Google Scholar

[31] Ham S, Choi S, Cho H. Photonic Organolead Halide Perovskite Artificial Synapse Capable of Accelerated Learning at Low Power Inspired by Dopamine-Facilitated Synaptic Activity. Adv Funct Mater, 2019, 29: 1806646 CrossRef Google Scholar

[32] Zhao S, Ni Z, Tan H, et al. Electroluminescent synaptic devices with logic functions. Nano Energy, 2018, 383-389. Google Scholar

[33] Zhao S, Wang Y, Huang W. Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity. Sci China Mater, 2019, 62: 1470-1478 CrossRef Google Scholar

[34] Gkoupidenis P, Koutsouras D A, Malliaras G G. Neuromorphic device architectures with global connectivity through electrolyte gating. Nat Commun, 2017, 8: 15448 CrossRef PubMed ADS Google Scholar

[35] Deisseroth K. Optogenetics. Nat Methods, 2011, 8: 26-29. Google Scholar

[36] Treichler D G. Are you missing the boat in training aids. Film Audio-Visual Commun, 1967, 1: 14-16. Google Scholar

[37] Wang G, Wang R, Kong W. Simulation of retinal ganglion cell response using fast independent component analysis.. Cogn Neurodyn, 2018, 12: 615-624 CrossRef PubMed Google Scholar

[38] Xiao Z, Huang J. Energy-Efficient Hybrid Perovskite Memristors and Synaptic Devices. Adv Electron Mater, 2016, 2: 1600100 CrossRef Google Scholar

[39] Bi G, Poo M. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. J Neurosci, 1998, 18: 10464-10472 CrossRef Google Scholar

[40] Kandel E R. Neuroscience: breaking down scientific barriers to the study of brain and mind.. Science, 2000, 290: 1113-1120 CrossRef PubMed Google Scholar

[41] Chih B. Control of Excitatory and Inhibitory Synapse Formation by Neuroligins. Science, 2005, 307: 1324-1328 CrossRef PubMed ADS Google Scholar

[42] Sturman B, Podivilov E, Gorkunov M. Origin of Stretched Exponential Relaxation for Hopping-Transport Models. Phys Rev Lett, 2003, 91: 176602 CrossRef PubMed ADS Google Scholar

[43] Yang Y, Lisberger S G. Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature, 2014, 510: 529-532 CrossRef PubMed ADS Google Scholar

[44] Qin S, Wang F, Liu Y. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4: 035022 CrossRef ADS arXiv Google Scholar

[45] Yu J J, Liang L Y, Hu L X. Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation. Nano Energy, 2019, 62: 772-780 CrossRef Google Scholar

[46] Kumar M, Abbas S, Kim J. All-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing. ACS Appl Mater Interfaces, 2018, 10: 34370-34376 CrossRef Google Scholar

[47] Zhou F, Zhou Z, Chen J. Optoelectronic resistive random access memory for neuromorphic vision sensors.. Nat Nanotechnol, 2019, 14: 776-782 CrossRef PubMed Google Scholar

[48] Ni Z, Wang Y, Liu L, et al. Hybrid structure of silicon nanocrystals and 2D WSe$_2$ for broadband optoelectronic synaptic devices. In: Proceedings of the 64th Annual IEEE International Electron Devices Meeting (IEDM), San Francisco, 2018. 887-890. Google Scholar

[49] Mueller T, Xia F, Avouris P. Graphene photodetectors for high-speed optical communications. Nat Photon, 2010, 4: 297-301 CrossRef Google Scholar

[50] Destexhe A, Marder E. Plasticity in single neuron and circuit computations. Nature, 2004, 431: 789-795 CrossRef PubMed ADS Google Scholar

[51] Zucker R S, Regehr W G. Short-Term Synaptic Plasticity. Annu Rev Physiol, 2002, 64: 355-405 CrossRef Google Scholar

[52] Abbott L F, Regehr W G. Synaptic computation. Nature, 2004, 431: 796-803. Google Scholar

[53] Hebb D O. Organization of behavior. J Physiol, 1949, 911: 335. Google Scholar

[54] Bliss T V P, Collingridge G L. A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 1993, 361: 31-39 CrossRef PubMed ADS Google Scholar

[55] Kandel E R. The Molecular Biology of Memory Storage: A Dialogue Between Genes and Synapses. Science, 2001, 294: 1030-1038 CrossRef PubMed ADS Google Scholar

[56] Lamprecht R, LeDoux J. Structural plasticity and memory.. Nat Rev Neurosci, 2004, 5: 45-54 CrossRef PubMed Google Scholar

[57] Wan C J, Zhu L Q, Zhou J M. Inorganic proton conducting electrolyte coupled oxide-based dendritic transistors for synaptic electronics. Nanoscale, 2014, 6: 4491-4497 CrossRef PubMed ADS Google Scholar

[58] Debanne D, Guérineau N C, G?hwiler B H. Paired-pulse facilitation and depression at unitary synapses in rat hippocampus: quantal fluctuation affects subsequent release.. J Physiol, 1996, 491: 163-176 CrossRef Google Scholar

[59] Hu S G, Liu Y, Chen T P. Emulating the paired-pulse facilitation of a biological synapse with a NiO$_{x}$-based memristor. Appl Phys Lett, 2013, 102: 183510 CrossRef ADS Google Scholar

[60] Liu Y H, Zhu L Q, Feng P. Freestanding Artificial Synapses Based on Laterally Proton-Coupled Transistors on Chitosan Membranes.. Adv Mater, 2015, 27: 5599-5604 CrossRef PubMed Google Scholar

[61] Liu G, Wang C, Zhang W. Organic Biomimicking Memristor for Information Storage and Processing Applications. Adv Electron Mater, 2016, 2: 1500298 CrossRef Google Scholar

[62] Atluri P P, Regehr W G. Determinants of the Time Course of Facilitation at the Granule Cell to Purkinje Cell Synapse. J Neurosci, 1996, 16: 5661-5671 CrossRef Google Scholar

[63] Qin S, Liu Y, Wang X, et al. Light-activated artificial synapse based on graphene hybrid phototransistors. In: Proceedings of Conference on Lasers and Electro-Optics (CLEO), San Jose, 2016. SW1R.4. Google Scholar

[64] Li B, Wei W, Yan X. Mimicking synaptic functionality with an InAs nanowire phototransistor. Nanotechnology, 2018, 29: 464004 CrossRef PubMed ADS Google Scholar

[65] Yang Y, He Y, Nie S. Light Stimulated IGZO-Based Electric-Double-Layer Transistors For Photoelectric Neuromorphic Devices. IEEE Electron Device Lett, 2018, 39: 897-900 CrossRef ADS Google Scholar

[66] Wang Y, Lv Z, Chen J. Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing.. Adv Mater, 2018, 30: 1802883 CrossRef PubMed Google Scholar

[67] Atkinson R C, Shiffrin R M. Human memory: a proposed system and its control processes. The Psychology of Learning and Motivation: Advances in Research and Theory, 1968, 2: 89-195. Google Scholar

[68] McGaugh J L. Memory-a Century of Consolidation. Science, 2000, 287: 248-251 CrossRef PubMed ADS Google Scholar

[69] Izquierdo I, McGaugh J L. Behavioural pharmacology and its contribution to the molecular basis of memory consolidation.. Behaval Pharmacol, 2000, 11: 517-534 CrossRef PubMed Google Scholar

[70] Lee M, Lee W, Choi S. Brain-Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity.. Adv Mater, 2017, 29: 1700951 CrossRef PubMed Google Scholar

[71] Dan Y, Poo M M. Spike timing-dependent plasticity: from synapse to perception.. Physiol Rev, 2006, 86: 1033-1048 CrossRef PubMed Google Scholar

[72] Mandal S, Long B, Jha R. Study of Synaptic Behavior in Doped Transition Metal Oxide-Based Reconfigurable Devices. IEEE Trans Electron Devices, 2013, 60: 4219-4225 CrossRef ADS Google Scholar

[73] He H K, Yang R, Zhou W. Small, 2018, 14: 1800079 CrossRef PubMed Google Scholar

[74] Yang C S, Shang D S, Chai Y S. Electrochemical-reaction-induced synaptic plasticity in MoOx-based solid state electrochemical cells. Phys Chem Chem Phys, 2017, 19: 4190-4198 CrossRef PubMed ADS Google Scholar

[75] Pilarczyk K, Podborska A, Lis M. Synaptic Behavior in an Optoelectronic Device Based on Semiconductor-Nanotube Hybrid. Adv Electron Mater, 2016, 2: 1500471 CrossRef Google Scholar

[76] Wang K, Dai S, Zhao Y, et al. Light-stimulated synaptic transistors fabricated by a facile solution process based on inorganic perovskite quantum dots and organic semiconductors. Small, 2019, 15: e1900010. Google Scholar

[77] Wang Z Q, Xu H Y, Li X H. Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor. Adv Funct Mater, 2012, 22: 2759-2765 CrossRef Google Scholar

[78] Li S, Zeng F, Chen C. Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J Mater Chem C, 2013, 1: 5292-5298 CrossRef Google Scholar

[79] Wang L G, Zhang W, Chen Y. Synaptic Plasticity and Learning Behaviors Mimicked in Single Inorganic Synapses of Pt/HfO$_{x}$/ZnO$_{x}$/TiN Memristive System. Nanoscale Res Lett, 2017, 12: 65 CrossRef PubMed ADS Google Scholar

[80] Froemke R C, Dan Y. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature, 2002, 416: 433-438 CrossRef PubMed ADS Google Scholar

[81] Kim H, Hwang S, Park J. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system. Nanotechnology, 2017, 28: 405202 CrossRef PubMed ADS Google Scholar

[82] Covi E, Brivio S, Serb A, et al. Analog memristive synapse in spiking networks implementing unsupervised learning. Front Neurosci, 2016, 10: 482. Google Scholar

[83] D'amour J A, Froemke R C. Inhibitory and excitatory spike-timing-dependent plasticity in the auditory cortex.. Neuron, 2015, 86: 514-528 CrossRef PubMed Google Scholar

[84] Chen Y, Wei Q, Yin J. Silicon-Based Hybrid Optoelectronic Devices with Synaptic Plasticity and Stateful Photoresponse. Adv Electron Mater, 2018, 4: 1800242 CrossRef Google Scholar

[85] Alibart F, Pleutin S, Bichler O. A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing. Adv Funct Mater, 2012, 22: 609-616 CrossRef Google Scholar

[86] Lengyel M, Kwag J, Paulsen O. Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves.. Nat Neurosci, 2005, 8: 1677-1683 CrossRef PubMed Google Scholar

[87] Dudman J T, Tsay D, Siegelbaum S A. A role for synaptic inputs at distal dendrites: instructive signals for hippocampal long-term plasticity.. Neuron, 2007, 56: 866-879 CrossRef PubMed Google Scholar

[88] Guyonneau R, VanRullen R, Thorpe S J. Neurons tune to the earliest spikes through STDP.. Neural Computation, 2005, 17: 859-879 CrossRef PubMed Google Scholar

[89] Shouval H Z, Wang S S, Wittenberg G M. Spike timing dependent plasticity: a consequence of more fundamental learning rules. Front Comput Neurosci, 2010, 4: 19. Google Scholar

[90] Abbott L F, Nelson S B. Synaptic plasticity: taming the beast.. Nat Neurosci, 2000, 3: 1178-1183 CrossRef PubMed Google Scholar

[91] Li Y, Zhong Y, Zhang J, et al. Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci Rep, 2014, 4: 4906. Google Scholar

[92] Li Y, Zhong Y, Xu L. Ultrafast Synaptic Events in a Chalcogenide Memristor. Sci Rep, 2013, 3: 1619 CrossRef PubMed ADS Google Scholar

[93] Wang J, Chen Y, Kong L A. Deep-ultraviolet-triggered neuromorphic functions in In-Zn-O phototransistors. Appl Phys Lett, 2018, 113: 151101 CrossRef ADS Google Scholar

[94] Martin S J, Grimwood P D, Morris R G M. Synaptic Plasticity and Memory: An Evaluation of the Hypothesis. Annu Rev Neurosci, 2000, 23: 649-711 CrossRef Google Scholar

[95] Rachmuth G, Shouval H Z, Bear M F. PNAS Plus: A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity. Proc Natl Acad Sci USA, 2011, 108: E1266-E1274 CrossRef PubMed ADS Google Scholar

[96] Kirkwood A, Rioult M G, Bear M F. Experience-dependent modification of synaptic plasticity in visual cortex. Nature, 1996, 381: 526-528 CrossRef PubMed ADS Google Scholar

[97] Jiang J, Hu W, Xie D. 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration.. Nanoscale, 2019, 11: 1360-1369 CrossRef PubMed Google Scholar

[98] Indiveri G, Chicca E, Douglas R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.. IEEE Trans Neural Netw, 2006, 17: 211-221 CrossRef PubMed Google Scholar

[99] Alquraishi W, Fu Y, Qiu W. Hybrid optoelectronic synaptic functionality realized with ion gel-modulated In2O3 phototransistors. Org Electron, 2019, 71: 72-78 CrossRef Google Scholar

[100] Cheng W, Liang R, Tian H. Proton Conductor Gated Synaptic Transistor Based on Transparent IGZO for Realizing Electrical and UV Light Stimulus. IEEE J Electron Devices Soc, 2019, 7: 38-45 CrossRef Google Scholar

[101] Jeon S, Song I, Lee S. Origin of high photoconductive gain in fully transparent heterojunction nanocrystalline oxide image sensors and interconnects.. Adv Mater, 2014, 26: 7102-7109 CrossRef PubMed Google Scholar

[102] Ahn S E, Song I, Jeon S. Metal oxide thin film phototransistor for remote touch interactive displays.. Adv Mater, 2012, 24: 2631-2636 CrossRef PubMed Google Scholar

[103] Wu Q, Wang J, Cao J. Photoelectric Plasticity in Oxide Thin Film Transistors with Tunable Synaptic Functions. Adv Electron Mater, 2018, 4: 1800556 CrossRef Google Scholar

[104] Gao S, Liu G, Yang H, et al. An oxide schottky junction artificial optoelectronic synapse. ACS Nano, 2019, 13: 2634-2642. Google Scholar

[105] Shao L, Wang H, Yang Y. Optoelectronic Properties of Printed Photogating Carbon Nanotube Thin Film Transistors and Their Application for Light-Stimulated Neuromorphic Devices. ACS Appl Mater Interfaces, 2019, 11: 12161-12169 CrossRef Google Scholar

[106] Tan H, Ni Z, Peng W. Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy, 2018, 52: 422-430 CrossRef Google Scholar

[107] Yin L, Han C, Zhang Q. Synaptic silicon-nanocrystal phototransistors for neuromorphic computing. Nano Energy, 2019, 63: 103859 CrossRef Google Scholar

[108] Li H K, Chen T P, Liu P. A light-stimulated synaptic transistor with synaptic plasticity and memory functions based on InGaZnO$_{x}$-Al$_{2}$O$_{3}$ thin film structure. J Appl Phys, 2016, 119: 244505 CrossRef ADS Google Scholar

[109] Hu D C, Yang R, Jiang L. ACS Appl Mater Interfaces, 2018, 10: 6463-6470 CrossRef Google Scholar

[110] Dai S, Wu X, Liu D. Light-Stimulated Synaptic Devices Utilizing Interfacial Effect of Organic Field-Effect Transistors. ACS Appl Mater Interfaces, 2018, 10: 21472-21480 CrossRef Google Scholar

[111] Gou G, Sun J, Qian C, et al. Artificial synapses based on biopolymer electrolyte-coupled SnO$_2$ nanowire transistors. J Mater Chem C, 2016, 4: 11110-11117. Google Scholar

[112] Liu Y, Huang W, Wang X. A Hybrid Phototransistor Neuromorphic Synapse. IEEE J Electron Devices Soc, 2019, 7: 13-17 CrossRef Google Scholar

[113] Guo Y B, Zhu L Q, Gao W T. Low-voltage protonic/photonic synergic coupled oxide phototransistor. Org Electron, 2019, 71: 31-35 CrossRef Google Scholar

[114] John R A, Liu F, Chien N A. Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.. Adv Mater, 2018, 30: 1800220 CrossRef PubMed Google Scholar

[115] Wang S, Chen C, Yu Z. Adv Mater, 2019, 31: 1806227 CrossRef PubMed Google Scholar

  • Figure 1

    (Color online) (a) Schematics of a biological synapse [38]@Copyright 2016 WILEY-VCH Verlag GmbH & Co. KGaA; (b) Schematics of artificial synaptic devices with 2 terminals or 3 terminals

  • Figure 2

    (Color online) (a) Dependence of the $\Delta$PSC amplitudes on gate voltage for a synaptic device based on graphene hybrid phototransistor; (b) dependence of $\Delta$IPSC and $\Delta$EPSC on spike duration for the graphene hybrid phototransistor [44]@copyright 2017 IOP Publishing Ltd.; (c) dependence of the EPSC on drain voltage for a optoelectronic neuromorphic device based on pn-junction-decorated oxide thin-film transistor [45]@Copyright 2019 Elsevier Ltd.; (d) dependence of the EPSC on photonic pulse intensity for an all-oxide-based highly transparent photonic synapse [46]@Copyright 2018 American Chemical Society; (e) dependence of EPSC and relaxation time on pulse width at different light intensities for a MoO$_{x}$optoelectronic resistive random access memory [47]@Copyright 2019 Springer Nature; (f) dependence of the EPSC on the duration time of the laser spikes with different wavelengths for the Si-NC/WSe$_{2}$synaptic device [48]@Copyright 2018 IEEE

  • Figure 3

    (Color online) (a) Paired-pulse facilitation at the granule cell to Purkinje cell synapse [62]@Copyright 1996 Society for Neuroscience; (b) the PPF behavior of a graphene hybrid phototransistor [63]@Copyright 2016 Optical Society of America; (c) PPF behaviors of an InAs nanowire phototransistor [64]@Copyright 2018 IOP Publishing Ltd.; (d) dependence of the PPF index on pulse intervals for the laser spikes with different gate biases [65]@Copyright 2018 IEEE; (e) dependence of the PPF index on pulse intervals for the laser spikes with different light pulse intensities; (f) dependence of the PPF index on pulse intervals for the laser spikes with different wavelengths [66]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 4

    Schematic illustration of a typical memory model in psychology [70]@Copyright 2017 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 5

    (Color online) Channel conductance change ($\Delta~G$ as a function of (a) presynaptic light pulse number, protectłinebreak (b) presynaptic light pulse intensity, and (c) presynaptic light pulse width [76]@Copyright 2019 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 6

    (Color online) (a) STDP measurements in hippocampal glutamatergic synapses [39]@Copyright 1998 Society for Neuroscience; (b) four forms of STDP [89]@Copyright 2010 Shouval, Wang and Wittenberg

  • Figure 7

    (Color online) STDP learning behaviors mimicked on a deep-ultraviolet-triggered InZnO phototransistors [93]@Copyright 2018 AIP Publishing. (a) and (b) The asymmetric Hebbian learning rule; (c) and (d) the symmetric Hebbian learning rule and the waveform of optical/electrical spike for achieving them

  • Figure 8

    (Color online) (a) Biological experience-dependent plasticity in the visual cortex [96]@Copyright 1996 Springer Nature; (b) EPSCs recorded in response to the stimulus train with different frequencies; (c) ${\Delta}S$ plotted as a function of presynaptic spike frequency [97]@Copyright 2019 The Royal Society of Chemistry; (d) ${\Delta}S$ after 50 light pulses plotted as a function of gate voltage [65]@Copyright 2018 IEEE

  • Figure 9

    (Color online) Schematic of calculation methods for energy consumption of single synaptic event in optoelectronic synaptic devices. (a) The 1st method; (b) the 2nd method; (c) the 3rd method

  • Figure 10

    (Color online) (a) Energy-band diagrams of an IGZO-based photonic neuromorphic device in dark conditions and under illumination; (b) the relationship between the activation energies ($E_{\rm~a}$) and the relaxation time constant for various AOSs [70]@Copyright 2017 WILEY-VCH Verlag GmbH & Co. KGaA; (c) the photonic operation concept of an IGZO-based TFT device; (d) illustration of atomic structures for N$\rm_R$ In$\rm_H$ and Ga$\rm_H$IGZO films [106]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA; (e) schematic energy-band diagrams for the formation of ${{\rm~V}_{\rm~O}}^{2+}$and ${{\rm~V}_{\rm~O}}^{2+}$/${\rm~O}_{\rm~i}$; (f) schematic energy vs. lattice relaxation curves of the (0) and (2+) charge state in $\alpha$- and $\beta$-type configurations (the dashed and solid arrowline denotes the ionization and recovery process, respectively);protectłinebreak (g) the EPSC of IGZO, SnO$_x$/IGZO and PP/SnO$_x$/IGZO devices after light stimulation [45]@Copyright 2019 Elsevier Ltd.

  • Figure 11

    (Color online) (a) Schematic operation mechanism of an artificial optoelectronicsynapse based on ITO/Nb:SrTiO$_3$heterojunction; (b) mimicry of human visual memory [107]@Copyright 2019 American Chemical Society

  • Figure 12

    (Color online) (a) The mechanism responsible for the synaptic behavior of the CdS/MWCNT-based device [75]@Copyright 2016 WILEY-VCH Verlag GmbH & Co. KGaA; (b) schematic of a printed SWCNT phototransistor device under illumination of different light wavelengths; (c) low-pass filtering characteristics of the printed SWCNT transistors device [108]@Copyright 2019 American Chemical Society; (d) energy band diagram of excitation, thermalization, recombination, and trapping processes in the InAs nanowire phototransistor [64]@Copyright 2018 IOP Publishing Ltd.; (e) schematic of an array of Si-NC-based synaptic devices; (f) the STS curve, resulting for the Si-NC film measured at 77 K, has been shifted so that the Fermi level is at 0 V; (g) schematic model for the electronic structure and carrier behavior of Si NCs [100]@Copyright 2018 Elsevier Ltd.; (h) schematic of the Si-NC/WSe$_2$synaptic device structure [48]@Copyright 2018 IEEE

  • Figure 13

    (Color online) Neuromorphic computing simulation for image recognition. (a) Schematic of a taste aversion learning process for the treatment of alcoholism; (b) implementation of taste aversion learning with a synaptic Si-NC phototransistor; (c) example images in the MNIST database after the binarization with a pixel threshold of 50; (d) the architecture of the spiking neural network; (e) receptive fields of all output neurons in the network trained with the L/E$^+$STDP model; (f) receptive fields of all output neurons in the network trained with the E$^-$/E$^+$STDP model [101]@Copyright 2019 Elsevier Ltd.

  • Figure 14

    (Color online) (a) Mechanism of IGZO-based synaptic transistor under UV-light stimulation [109]@Copyright 2016 AIP Publishing; (b) schematic illustrations of the band diagram of the ZnO$_{1-x}$/AlO$_{y}$heterojunction [110]@Copyright 2018 American Chemical Society; (c) schematic diagram of a light-stimulated C8-BTBT synaptic transistors array; protectłinebreak (d) fabrication process of the T-shape transistors array; (e) dynamic learning and forgetting process of the T-shape synaptic transistors array [111]@Copyright 2018 American Chemical Society

  • Figure 15

    (Color online) (a) Mechanism of the IGZO-based EDL transistors [65]@Copyright 2018 IEEE; (b) mechanism of the MoS$_{2}$-based EDL transistors; (c) schematic of the photonic MoS$_{2}$synapse to function as both high-pass and low-pass photonic filters; (d) the spatiotemporal correlation effect; (e) the changes in synaptic weight ($\Delta~W$ as function of

  • Figure 16

    (Color online) Classical conditioning Pavlov's dog experiment emulated by MoS$_{2}$neuristors [112]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

  • Figure 17

    (Color online) (a) The time response of an NOR logic operation emulated by the graphene/SWCNTs hybrid phototransistor [44]@Copyright 2017 IOP Publishing Ltd.; (b) schematics of a two-terminal visible light transparent device based on ZnO/In$_{2}$O$_{3}$; (c) schematic illustrations of the working mechanism of the photonic synapse [46]@Copyright 2018 American Chemical Society; (d) endurance property of the light-programmed state and electric erased state of the photonic synapses based on inorganic perovskite quantum dots; (e) energy diagram of photonic synapses based on inorganic perovskite quantum dots during light programming operation and during electrical erasing operation under dark protectłinebreak condition [66]@Copyright 2018 WILEY-VCH Verlag GmbH & Co. KGaA

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