Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates

Flexible metal oxide/graphene oxide hybrid multi-gate neuromorphic transistors are fabricated on flexible conducting graphene substrates. Dendritic integrations in both spatial and temporal modes are emulated, and spatiotemporal correlated logics are obtained. A proof-of-principle visual system model for emulating Lobula Giant Motion Detector neuron is also investigated. The results are of great significance for flexible sensors and neuromorphic cognitive systems.

[1]  Yi Shi,et al.  Organic/inorganic hybrid synaptic transistors gated by proton conducting methylcellulose films , 2016 .

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

[3]  Ali Khiat,et al.  Emulating short-term synaptic dynamics with memristive devices , 2015, Scientific Reports.

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

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

[6]  Sung-Jin Choi,et al.  Carbon Nanotube Synaptic Transistor Network for Pattern Recognition. , 2015, ACS applied materials & interfaces.

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

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

[9]  Tingrui Pan,et al.  Flexible Transparent Iontronic Film for Interfacial Capacitive Pressure Sensing , 2015, Advanced materials.

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

[11]  C. Battaglia,et al.  Room Temperature Oxide Deposition Approach to Fully Transparent, All‐Oxide Thin‐Film Transistors , 2015, Advanced materials.

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

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

[14]  E. Vianello,et al.  HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks , 2015, IEEE Transactions on Electron Devices.

[15]  Jong-Hyun Ahn,et al.  Graphene for displays that bend. , 2014, Nature nanotechnology.

[16]  Qing Wan,et al.  Laterally Coupled Synaptic Transistors Gated by Proton Conducting Sodium Alginate Films , 2014, IEEE Electron Device Letters.

[17]  Qing Wan,et al.  Classical Conditioning Mimicked in Junctionless IZO Electric-Double-Layer Thin-Film Transistors , 2014, IEEE Electron Device Letters.

[18]  Zhenqiang Ma,et al.  Highly stretchable carbon nanotube transistors with ion gel gate dielectrics. , 2014, Nano letters.

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

[20]  Ludger Koenders,et al.  Nanoscale 2013 , 2014 .

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

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

[23]  Takeshi Matsui,et al.  Graphene oxide nanosheet with high proton conductivity. , 2013, Journal of the American Chemical Society.

[24]  Alex Ming Shen,et al.  A Carbon Nanotube Synapse with Dynamic Logic and Learning , 2013, Advanced materials.

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

[26]  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.

[27]  M. Pickett,et al.  A scalable neuristor built with Mott memristors. , 2013, Nature materials.

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

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

[30]  Jong-Hyun Ahn,et al.  All graphene-based thin film transistors on flexible plastic substrates. , 2012, Nano letters.

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

[32]  Jong-Hyun Ahn,et al.  Extremely efficient flexible organic light-emitting diodes with modified graphene anode , 2012, Nature Photonics.

[33]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

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

[35]  Woojin Park,et al.  Nonvolatile memory functionality of ZnO nanowire transistors controlled by mobile protons. , 2011, ACS nano.

[36]  M. Häusser,et al.  The single dendritic branch as a fundamental functional unit in the nervous system , 2010, Current Opinion in Neurobiology.

[37]  Kwang S. Kim,et al.  Roll-to-roll production of 30-inch graphene films for transparent electrodes. , 2010, Nature nanotechnology.

[38]  Hongtao Yuan,et al.  Hydrogenation-induced surface polarity recognition and proton memory behavior at protic-ionic-liquid/oxide electric-double-layer interfaces. , 2010, Journal of the American Chemical Society.

[39]  Kwang S. Kim,et al.  Large-scale pattern growth of graphene films for stretchable transparent electrodes , 2009, Nature.

[40]  J. Nadal,et al.  What can we learn from synaptic weight distributions? , 2007, Trends in Neurosciences.

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

[42]  Stephen R. Williams,et al.  Spatial compartmentalization and functional impact of conductance in pyramidal neurons , 2004, Nature Neuroscience.

[43]  C. Koch,et al.  Multiplicative computation in a visual neuron sensitive to looming , 2002, Nature.

[44]  Paul F. M. J. Verschure,et al.  Collision avoidance using a model of the locust LGMD neuron , 2000, Robotics Auton. Syst..

[45]  R. Yuste,et al.  Linear Summation of Excitatory Inputs by CA1 Pyramidal Neurons , 1999, Neuron.

[46]  G. Laurent,et al.  Computation of Object Approach by a Wide-Field, Motion-Sensitive Neuron , 1999, The Journal of Neuroscience.

[47]  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.

[48]  F. Rind,et al.  Neural network based on the input organization of an identified neuron signaling impending collision. , 1996, Journal of neurophysiology.

[49]  Bartlett W. Mel.  Synaptic integration in an excitable dendritic tree. , 1993, Journal of neurophysiology.