A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems

Resistive (or memristive) switching devices based on metal oxides find applications in memory, logic and neuromorphic computing systems. Their small area, low power operation, and high functionality meet the challenges of brain-inspired computing aiming at achieving a huge density of active connections (synapses) with low operation power. This work presents a new artificial synapse scheme, consisting of a memristive switch connected to 2 transistors responsible for gating the communication and learning operations. Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes. Experiments with integrated artificial synapses demonstrate STDP with stochastic behavior due to (i) the natural variability of set/reset processes in the nanoscale switch, and (ii) the different response of the switch to a given stimulus depending on the initial state. Experimental results are confirmed by model-based simulations of the memristive switching. Finally, system-level simulations of a 2-layer neural network and a simplified STDP model show random learning and recognition of patterns.

[1]  K. D. Cantley,et al.  Spike-Timing-Dependent Plasticity Using Biologically Realistic Action Potentials and Low-Temperature Materials , 2013, IEEE Transactions on Nanotechnology.

[2]  Y. J. Lee,et al.  Extended scalability of perpendicular STT-MRAM towards sub-20nm MTJ node , 2011, 2011 International Electron Devices Meeting.

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

[4]  Omid Kavehei,et al.  Multiprotocol-induced plasticity in artificial synapses. , 2014, Nanoscale.

[5]  S. Wang,et al.  Malleability of Spike-Timing-Dependent Plasticity at the CA3–CA1 Synapse , 2006, The Journal of Neuroscience.

[6]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[7]  Kinam Kim,et al.  A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures. , 2011, Nature materials.

[8]  Giacomo Indiveri,et al.  Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

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

[11]  Fabien Alibart,et al.  Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse , 2010, IEEE Transactions on Electron Devices.

[12]  S. Ambrogio,et al.  Spike-timing dependent plasticity in a transistor-selected resistive switching memory , 2013, Nanotechnology.

[13]  Carver A. Mead,et al.  A single-transistor silicon synapse , 1996 .

[14]  H. Hwang,et al.  Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device , 2011, Nanotechnology.

[15]  D. Ielmini,et al.  Phase change materials in non-volatile storage , 2011 .

[16]  Alessandro Calderoni,et al.  Statistical Fluctuations in HfOx Resistive-Switching Memory: Part I - Set/Reset Variability , 2014, IEEE Transactions on Electron Devices.

[17]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[18]  M. Poo,et al.  Calcium stores regulate the polarity and input specificity of synaptic modification , 2000, Nature.

[19]  V. Cros,et al.  Spin-torque building blocks. , 2014, Nature Materials.

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

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

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

[23]  H. Hwang,et al.  Nanoscale (∼10nm) 3D vertical ReRAM and NbO2 threshold selector with TiN electrode , 2013, 2013 IEEE International Electron Devices Meeting.

[24]  D. Koshland.  Frontiers in neuroscience. , 1988, Science.

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

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

[27]  D. Querlioz,et al.  Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture , 2012, IEEE Transactions on Electron Devices.

[28]  Y. Dan,et al.  Spike timing-dependent plasticity: a Hebbian learning rule. , 2008, Annual review of neuroscience.

[29]  Gregory S. Snider,et al.  Spike-timing-dependent learning in memristive nanodevices , 2008, 2008 IEEE International Symposium on Nanoscale Architectures.

[30]  K. Tsunoda,et al.  Reduction in the reset current in a resistive random access memory consisting of NiOx brought about by reducing a parasitic capacitance , 2008 .

[31]  S. Ambrogio,et al.  Analytical Modeling of Oxide-Based Bipolar Resistive Memories and Complementary Resistive Switches , 2014, IEEE Transactions on Electron Devices.

[32]  S. Dimauro,et al.  Primary LAMP-2 deficiency causes X-linked vacuolar cardiomyopathy and myopathy (Danon disease) , 2000, Nature.

[33]  S. Kurajica.  Phase change materials , 2007 .

[34]  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.

[35]  Frederick T. Chen,et al.  Low power and high speed bipolar switching with a thin reactive Ti buffer layer in robust HfO2 based RRAM , 2008, 2008 IEEE International Electron Devices Meeting.

[36]  Andreas Mayr,et al.  CrossNets: High‐Performance Neuromorphic Architectures for CMOL Circuits , 2003, Annals of the New York Academy of Sciences.

[37]  D. Ielmini,et al.  Modeling the Universal Set/Reset Characteristics of Bipolar RRAM by Field- and Temperature-Driven Filament Growth , 2011, IEEE Transactions on Electron Devices.

[38]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[39]  C. Wright,et al.  Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials , 2011, Advanced materials.

[40]  H. Kim,et al.  RRAM-based synapse for neuromorphic system with pattern recognition function , 2012, 2012 International Electron Devices Meeting.

[41]  Yong Liu,et al.  Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems , 2013, IEEE Transactions on Electron Devices.

[42]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[43]  J. Gasser,et al.  Low Energy? , 2019, Plastics Engineering.

[44]  A. Calderoni,et al.  Performance comparison of O-based and Cu-based ReRAM for high-density applications , 2014, 2014 IEEE 6th International Memory Workshop (IMW).

[45]  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.

[46]  E. Vianello,et al.  Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses , 2013, IEEE Transactions on Electron Devices.

[47]  I. Baek,et al.  Multi-layer cross-point binary oxide resistive memory (OxRRAM) for post-NAND storage application , 2005, IEEE InternationalElectron Devices Meeting, 2005. IEDM Technical Digest..

[48]  S. Balatti,et al.  Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part I: Experimental Study , 2012, IEEE Transactions on Electron Devices.