Recent progress on 2D materials-based artificial synapses

Abstract Artificial synapses in neuromorphic computing systems hold potential to emulate biological synaptic plasticity to achieve brain-like computation and autonomous learning behaviors in non-von-Neumann systems. 2D materials, such as graphene, graphene oxide, hexagonal boron nitride, transition metal dichalcogenides, transition metal oxides, 2D perovskite, and black phosphorous, have been explored to achieve many functionalities of biological synapses due to their unique electronic, optoelectronic, electrochemical, and mechanical properties that are lacking in bulk materials. This review features the current development in the state-of-the-art artificial synaptic electronic devices based on 2D materials. The structures of these devices are first discussed according to their number of terminals (two-, three-, four-, and multi-terminals) and geometric layouts (vertical, horizontal, hybrid). Since different 2D materials have been utilized to fabricate these devices, their underlying physical mechanisms and principles are further discussed, and their artificial neuron synaptic functionalities and performances are analyzed and contrasted. Finally, a summary of the current research status and major achievements is concluded, and the outlooks and perspectives for this emerging and vibrant field and the potential applications of these devices for neuromorphic computing are presented.

[1]  Ru Huang,et al.  Dual-Gated MoS2 Neuristor for Neuromorphic Computing. , 2019, ACS applied materials & interfaces.

[2]  Aaron Thean,et al.  A Fully Printed Flexible MoS2 Memristive Artificial Synapse with Femtojoule Switching Energy , 2019, Advanced Electronic Materials.

[3]  Tania Roy,et al.  Electronic synapses with near-linear weight update using MoS2/graphene memristors , 2019, Applied Physics Letters.

[4]  T. Hou,et al.  A Fluorographene‐Based Synaptic Transistor , 2019, Advanced Materials Technologies.

[5]  B. Akgenc Two-dimensional black arsenic for Li-ion battery applications: a DFT study , 2019, Journal of Materials Science.

[6]  Jianhui Zhao,et al.  Vacancy-Induced Synaptic Behavior in 2D WS2 Nanosheet-Based Memristor for Low-Power Neuromorphic Computing. , 2019, Small.

[7]  Xin Huang,et al.  Artificial Synapses Based on Multiterminal Memtransistors for Neuromorphic Application , 2019, Advanced Functional Materials.

[8]  Tae Whan Kim,et al.  Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing , 2019, NPG Asia Materials.

[9]  Donhee Ham,et al.  Vertical MoS2 Double-Layer Memristor with Electrochemical Metallization as an Atomic-Scale Synapse with Switching Thresholds Approaching 100 mV. , 2019, Nano letters.

[10]  Rabia Riaz,et al.  Silver‐Adapted Diffusive Memristor Based on Organic Nitrogen‐Doped Graphene Oxide Quantum Dots (N‐GOQDs) for Artificial Biosynapse Applications , 2019, Advanced Functional Materials.

[11]  Zhi Jin,et al.  Reconfigurable Artificial Synapses between Excitatory and Inhibitory Modes Based on Single‐Gate Graphene Transistors , 2019, Advanced Electronic Materials.

[12]  Biao Liu,et al.  Proton–electron-coupled MoS2 synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning , 2019, Journal of Materials Chemistry C.

[13]  Dong Wang,et al.  Selective growth of monolayer semiconductors for diverse synaptic junctions , 2018, 2D Materials.

[14]  Bin Wu,et al.  MoS2 Memristors Exhibiting Variable Switching Characteristics toward Biorealistic Synaptic Emulation. , 2018, ACS nano.

[15]  Huaqiang Wu,et al.  Graphene Oxide Quantum Dots Based Memristors with Progressive Conduction Tuning for Artificial Synaptic Learning , 2018, Advanced Functional Materials.

[16]  Eric Pop,et al.  Electronic synapses made of layered two-dimensional materials , 2018, Nature Electronics.

[17]  M. Yun,et al.  Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing , 2018, Advanced materials.

[18]  Jung Min Lee,et al.  Synaptic Barristor Based on Phase‐Engineered 2D Heterostructures , 2018, Advanced materials.

[19]  T. Hou,et al.  Programmable Synaptic Metaplasticity and below Femtojoule Spiking Energy Realized in Graphene-Based Neuromorphic Memristor. , 2018, ACS applied materials & interfaces.

[20]  Arindam Basu,et al.  Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity , 2018, Advanced materials.

[21]  Yongli He,et al.  Electric-double-layer transistors for synaptic devices and neuromorphic systems , 2018 .

[22]  Yuchao Yang,et al.  Ion Gated Synaptic Transistors Based on 2D van der Waals Crystals with Tunable Diffusive Dynamics , 2018, Advanced materials.

[23]  M. Hersam,et al.  Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide , 2018, Nature.

[24]  Barry P Rand,et al.  Extremely Low Operating Current Resistive Memory Based on Exfoliated 2D Perovskite Single Crystals for Neuromorphic Computing. , 2017, ACS nano.

[25]  Yusuf Leblebici,et al.  Neuromorphic computing with multi-memristive synapses , 2017, Nature Communications.

[26]  Qing Wan,et al.  2D MoS2 Neuromorphic Devices for Brain-Like Computational Systems. , 2017, Small.

[27]  Young Sun,et al.  A Synaptic Transistor based on Quasi‐2D Molybdenum Oxide , 2017, Advanced materials.

[28]  M. Marinella,et al.  A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. , 2017, Nature materials.

[29]  Saptarshi Das,et al.  Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS2 Transistors. , 2017, ACS nano.

[30]  Pritish Narayanan,et al.  Neuromorphic computing using non-volatile memory , 2017 .

[31]  Ling-an Kong,et al.  Ion-gel gated field-effect transistors with solution-processed oxide semiconductors for bioinspired artificial synapses , 2016 .

[32]  Yuchao Yang,et al.  Nonassociative learning implementation by a single memristor-based multi-terminal synaptic device. , 2016, Nanoscale.

[33]  Yi Shi,et al.  Long-Term Synaptic Plasticity Emulated in Modified Graphene Oxide Electrolyte Gated IZO-Based Thin-Film Transistors. , 2016, ACS applied materials & interfaces.

[34]  Yang Hui Liu,et al.  Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane. , 2016, ACS applied materials & interfaces.

[35]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

[36]  Yang Hui Liu,et al.  Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates , 2016, Advanced materials.

[37]  John F. Donegan,et al.  Associative Enhancement of Time Correlated Response to Heterogeneous Stimuli in a Neuromorphic Nanowire Device , 2016 .

[38]  K. Yoo,et al.  Memristive Switching in Bi(1-x)Sb(x) Nanowires. , 2016, ACS applied materials & interfaces.

[39]  Ji Rong Sun,et al.  Ternary Synaptic Plasticity Arising from Memdiode Behavior of TiOx Single Nanowires , 2016 .

[40]  F. Xia,et al.  Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications , 2016, Advanced materials.

[41]  George G. Malliaras,et al.  Synaptic plasticity functions in an organic electrochemical transistor , 2015 .

[42]  Wei Lu,et al.  Temporal information encoding in dynamic memristive devices , 2015 .

[43]  Yi Yang,et al.  Graphene Dynamic Synapse with Modulatable Plasticity. , 2015, Nano letters.

[44]  G. Malliaras,et al.  Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors , 2015, Advanced materials.

[45]  Qing Wan,et al.  Proton‐Conducting Graphene Oxide‐Coupled Neuron Transistors for Brain‐Inspired Cognitive Systems , 2015, Advanced materials.

[46]  Yang Hui Liu,et al.  Freestanding Artificial Synapses Based on Laterally Proton‐Coupled Transistors on Chitosan Membranes , 2015, Advanced materials.

[47]  Wei Lu,et al.  Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics , 2015 .

[48]  Sumio Hosaka,et al.  Associative memory realized by a reconfigurable memristive Hopfield neural network , 2015, Nature Communications.

[49]  A. Bessonov,et al.  Layered memristive and memcapacitive switches for printable electronics. , 2015, Nature materials.

[50]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[51]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[52]  Liangbing Hu,et al.  Approaching the limits of transparency and conductivity in graphitic materials through lithium intercalation , 2014, Nature Communications.

[53]  Chung Lam,et al.  Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array , 2014, Front. Neurosci..

[54]  Xianfan Xu,et al.  Phosphorene: an unexplored 2D semiconductor with a high hole mobility. , 2014, ACS nano.

[55]  F. Xia,et al.  Rediscovering black phosphorus as an anisotropic layered material for optoelectronics and electronics , 2014, Nature Communications.

[56]  Xianfan Xu,et al.  Phosphorene: an unexplored 2D semiconductor with a high hole mobility. , 2014, ACS nano.

[57]  Yi Shi,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems , 2013, Nature Communications.

[58]  Jian Shi,et al.  A correlated nickelate synaptic transistor , 2013, Nature Communications.

[59]  Shimeng Yu,et al.  Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.

[60]  B. Cho,et al.  Analog neuromorphic module based on carbon nanotube synapses. , 2013, ACS nano.

[61]  Enrico Macii,et al.  The Human Brain Project and neuromorphic computing. , 2013, Functional neurology.

[62]  Shimeng Yu,et al.  A Low Energy Oxide‐Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation , 2013, Advanced materials.

[63]  Li Qiang Zhu,et al.  Self-assembled dual in-plane gate thin-film transistors gated by nanogranular SiO2 proton conductors for logic applications. , 2013, Nanoscale.

[64]  Wen-Jen Chiang,et al.  Application of in-cell touch sensor using photo-leakage current in dual gate a-InGaZnO thin-film transistors , 2012 .

[65]  Andrew Mugler,et al.  Protein logic: a statistical mechanical study of signal integration at the single-molecule level. , 2012, Biophysical journal.

[66]  T. Morie,et al.  Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks , 2012 .

[67]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[68]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[69]  Matthew D. Pickett,et al.  Two‐ and Three‐Terminal Resistive Switches: Nanometer‐Scale Memristors and Memistors , 2011 .

[70]  Paul E. Hasler,et al.  Floating Gate Synapses With Spike-Time-Dependent Plasticity , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[71]  Yung-Hui Yeh,et al.  Influence of Passivation Layers on Characteristics of a-InGaZnO Thin-Film Transistors , 2011, IEEE Electron Device Letters.

[72]  Yihong Wu,et al.  Hysteresis of electronic transport in graphene transistors. , 2010, ACS nano.

[73]  Zhiyong Li,et al.  Ionic/Electronic Hybrid Materials Integrated in a Synaptic Transistor with Signal Processing and Learning Functions , 2010, Advanced materials.

[74]  J. Shan,et al.  Atomically thin MoS₂: a new direct-gap semiconductor. , 2010, Physical review letters.

[75]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[76]  C. Gamrat,et al.  An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse , 2009, 0907.2540.

[77]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[78]  Andre K. Geim,et al.  Electric Field Effect in Atomically Thin Carbon Films , 2004, Science.

[79]  Bartlett W. Mel,et al.  Computational subunits in thin dendrites of pyramidal cells , 2004, Nature Neuroscience.

[80]  M. Bennett,et al.  Electrical Coupling and Neuronal Synchronization in the Mammalian Brain , 2004, Neuron.

[81]  S. Möller,et al.  A polymer/semiconductor write-once read-many-times memory , 2003, Nature.

[82]  Vittorio Dante,et al.  A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory , 2003, IEEE Trans. Neural Networks.

[83]  Bartlett W. Mel,et al.  Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell , 2003, Neuron.

[84]  J. Magee Dendritic integration of excitatory synaptic input , 2000, Nature Reviews Neuroscience.

[85]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[86]  Andreas G. Andreou,et al.  Analog VLSI neuromorphic image acquisition and pre-processing systems , 1995, Neural Networks.

[87]  Tadashi Shibata,et al.  A functional MOS transistor featuring gate-level weighted sum and threshold operations , 1992 .

[88]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[89]  M. Dresselhaus,et al.  Structural characterization of ion-implanted graphite , 1982 .

[90]  L. Chua Memristor-The missing circuit element , 1971 .

[91]  Fabien Alibart,et al.  Synaptic Plasticity with Memristive Nanodevices , 2017 .

[92]  Sapan Agarwal,et al.  Li‐Ion Synaptic Transistor for Low Power Analog Computing , 2017, Advanced materials.

[93]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[94]  Xue-Bing Yin,et al.  Synaptic Metaplasticity Realized in Oxide Memristive Devices , 2016, Advanced materials.

[95]  Bin Ding,et al.  Critical Reviews in Solid State and Materials Sciences , 2012 .

[96]  R. Silver Neuronal arithmetic , 2010, Nature Reviews Neuroscience.

[97]  Giacomo Indiveri,et al.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity , 2006, IEEE Transactions on Neural Networks.

[98]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[99]  A. Thakoor,et al.  Design of parallel hardware neural network systems from custom analog VLSI 'building block' chips , 1989, International 1989 Joint Conference on Neural Networks.