Synaptic plasticity and memory functions achieved in a WO3−x-based nanoionics device by using the principle of atomic switch operation

A compact neuromorphic nanodevice with inherent learning and memory properties emulating those of biological synapses is the key to developing artificial neural networks rivaling their biological counterparts. Experimental results showed that memorization with a wide time scale from volatile to permanent can be achieved in a WO3-x-based nanoionics device and can be precisely and cumulatively controlled by adjusting the device's resistance state and input pulse parameters such as the amplitude, interval, and number. This control is analogous to biological synaptic plasticity including short-term plasticity, long-term potentiation, transition from short-term memory to long-term memory, forgetting processes for short- and long-term memory, learning speed, and learning history. A compact WO3-x-based nanoionics device with a simple stacked layer structure should thus be a promising candidate for use as an inorganic synapse in artificial neural networks due to its striking resemblance to the biological synapse.

[1]  T. Hasegawa,et al.  Atomic Switch: Atom/Ion Movement Controlled Devices for Beyond Von‐Neumann Computers , 2012, Advanced materials.

[2]  D. Drachman Do we have brain to spare? , 2005, Neurology.

[3]  Ennio Mingolla,et al.  From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain , 2011, Computer.

[4]  Jae Hyuck Jang,et al.  Atomic structure of conducting nanofilaments in TiO2 resistive switching memory. , 2010, Nature nanotechnology.

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

[6]  Masakazu Aono,et al.  Oxygen migration process in the interfaces during bipolar resistance switching behavior of WO3−x-based nanoionics devices , 2012 .

[7]  John von Neumann The Principles of Large-Scale Computing Machines , 1981, IEEE Ann. Hist. Comput..

[8]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[9]  T. Hasegawa,et al.  Learning Abilities Achieved by a Single Solid‐State Atomic Switch , 2010, Advanced materials.

[10]  M. Kozicki,et al.  A Low-Power Nonvolatile Switching Element Based on Copper-Tungsten Oxide Solid Electrolyte , 2006, IEEE Transactions on Nanotechnology.

[11]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

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

[13]  K. Terabe,et al.  Quantized conductance atomic switch , 2005, Nature.

[14]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[15]  Piotr Dudek,et al.  Compact silicon neuron circuit with spiking and bursting behaviour , 2008, Neural Networks.

[16]  Shimeng Yu,et al.  An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation , 2011, IEEE Transactions on Electron Devices.

[17]  T. Hasegawa,et al.  Controlling the Synaptic Plasticity of a Cu2S Gap‐Type Atomic Switch , 2012 .

[18]  J. Yang,et al.  Direct Identification of the Conducting Channels in a Functioning Memristive Device , 2010, Advanced materials.

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

[20]  H. Ebbinghaus Memory A Contribution Toexperimental Psychology , 1913 .

[21]  Masakazu Aono,et al.  On-demand nanodevice with electrical and neuromorphic multifunction realized by local ion migration. , 2012, ACS nano.

[22]  T. Hasegawa,et al.  Conductance quantization and synaptic behavior in a Ta2O5-based atomic switch , 2012, Nanotechnology.

[23]  Audrius V. Avizienis,et al.  Emergent Criticality in Complex Turing B‐Type Atomic Switch Networks , 2012, Advanced materials.

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

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

[26]  Massimiliano Versace,et al.  The brain of a new machine , 2010, IEEE Spectrum.

[27]  Paolo Lugli,et al.  Science and Engineering Beyond Moore's Law , 2012, Proceedings of the IEEE.

[28]  Dmitri B. Strukov,et al.  Nanotechnology: Smart connections , 2011, Nature.

[29]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[30]  Dharmendra S. Modha,et al.  The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.