Short-Term and Long-Term Plasticity Mimicked in Low-Voltage Ag/GeSe/TiN Electronic Synapse

The electronic synapse, which can vividly emulate short-term and long-term plasticity, as well as voltage sensitivity, in the bio-synapse, is the vital device foundation for brain-inspired neuromorphic computing. In this letter, we propose a Ag/GeSe/TiN memristor as an electronic synapse for brain-inspired neuromorphic applications. Due to the electromigration and diffusion of Ag cation, the volatile and non-volatile switching behaviours are coexistent in this device. Various synaptic functions, including short-term plasticity, long-term plasticity, pair-pulse facilitation, and spike timing-dependent plasticity, have been successfully eliminated in Ag/GeSe/TiN devices. Furthermore, all the synaptic functions are induced by the spiking stimuli with amplitudes of several hundred millivolts. All the results demonstrate that the Ag/GeSe/TiN device has great potential for brain-inspired computing systems in the future.

[1]  Qing Wan,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems. , 2014, Nature communications.

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

[3]  J. Joshua Yang,et al.  Synaptic electronics and neuromorphic computing , 2016, Science China Information Sciences.

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

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

[6]  L. Abbott,et al.  Synaptic computation , 2004, Nature.

[7]  Wentao Xu,et al.  Organometal Halide Perovskite Artificial Synapses , 2016, Advanced materials.

[8]  X. Miao,et al.  Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems , 2014, Scientific Reports.

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

[10]  Sarma B. K. Vrudhula,et al.  Demonstration of spike timing dependent plasticity in CBRAM devices with silicon neurons , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[11]  Alexantrou Serb,et al.  HfO2-based memristors for neuromorphic applications , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

[12]  T. Bliss,et al.  A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.

[13]  Bin Gao,et al.  Oxide-based analog synapse: Physical modeling, experimental characterization, and optimization , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

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

[15]  D. Feldman.  The Spike-Timing Dependence of Plasticity , 2012, Neuron.

[16]  Qing Wan,et al.  Short-Term Plasticity and Synaptic Filtering Emulated in Electrolyte-Gated IGZO Transistors , 2016, IEEE Electron Device Letters.

[17]  Hangbing Lv,et al.  Emulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor , 2017, IEEE Electron Device Letters.

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

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

[20]  Qi Liu,et al.  Eliminating Negative‐SET Behavior by Suppressing Nanofilament Overgrowth in Cation‐Based Memory , 2016, Advanced materials.

[21]  S. J. Martin,et al.  Synaptic plasticity and memory: an evaluation of the hypothesis. , 2000, Annual review of neuroscience.

[22]  F. Zhuge,et al.  Ultrasensitive Memristive Synapses Based on Lightly Oxidized Sulfide Films , 2017, Advanced materials.

[23]  Runchen Fang,et al.  A CMOS-compatible electronic synapse device based on Cu/SiO2/W programmable metallization cells , 2016, Nanotechnology.