A 4-Transistors/1-Resistor Hybrid Synapse Based on Resistive Switching Memory (RRAM) Capable of Spike-Rate-Dependent Plasticity (SRDP)

Mimicking the cognitive functions of the brain in hardware is a primary challenge for several fields, including device physics, neuromorphic engineering, and biological neuroscience. A key element in cognitive hardware systems is the ability to learn via biorealistic plasticity rules, combined with the area scaling capability to enable integration of high-density neuron/synapse networks. To this purpose, resistive switching memory (RRAM) devices have recently attracted a strong interest as potential synaptic elements. Here, we present a novel hybrid 4-transistors/1-resistor synapse capable of spike-rate-dependent plasticity. The frequency-dependent learning behavior of the synapse is shown by experiments on HfO2 RRAM devices. Unsupervised learning, update, and recognition of one or more visual patterns in sequence is demonstrated at the level of neural network, thus, supporting the feasibility of hybrid CMOS/RRAM integrated circuits matching the learning capability in the human brain.

[1]  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.

[2]  Yoon-Ha Jeong,et al.  Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems , 2015, IEEE Electron Device Letters.

[3]  W. Gerstner,et al.  Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.

[4]  Alessandro Calderoni,et al.  Postcycling Degradation in Metal-Oxide Bipolar Resistive Switching Memory , 2016, IEEE Transactions on Electron Devices.

[5]  Giacomo Indiveri,et al.  Memory and Information Processing in Neuromorphic Systems , 2015, Proceedings of the IEEE.

[6]  M. Bear,et al.  This paper was presented at a colloquium entitled ‘ ‘ Memory : Recording Experience in Cells and Circuits , ’ ’ organized by , 1996 .

[7]  Simone Balatti,et al.  A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems , 2015, Front. Neurosci..

[8]  Giacomo Indiveri,et al.  Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[9]  J. Pfister,et al.  A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations , 2011, Proceedings of the National Academy of Sciences.

[10]  Giacomo Indiveri,et al.  A differential memristive synapse circuit for on-line learning in neuromorphic computing systems , 2017, ArXiv.

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

[12]  Christopher D. Manning,et al.  Advances in natural language processing , 2015, Science.

[13]  Alessandro Calderoni,et al.  Voltage-Controlled Cycling Endurance of HfOx-Based Resistive-Switching Memory , 2015, IEEE Transactions on Electron Devices.

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Mirko Hansen,et al.  Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition , 2017, Front. Neurosci..

[16]  Mirko Hansen,et al.  Memristive Hebbian Plasticity Model: Device Requirements for the Emulation of Hebbian Plasticity Based on Memristive Devices , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[17]  D. Ielmini,et al.  Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[18]  Timothée Masquelier,et al.  Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..

[19]  Frederick T. Chen,et al.  Evidence and solution of over-RESET problem for HfOX based resistive memory with sub-ns switching speed and high endurance , 2010, 2010 International Electron Devices Meeting.

[20]  Tuo-Hung Hou,et al.  3D synaptic architecture with ultralow sub-10 fJ energy per spike for neuromorphic computation , 2014, 2014 IEEE International Electron Devices Meeting.

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

[22]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[23]  D. Ielmini,et al.  Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-time unsupervised machine learning , 2016, 2016 IEEE Symposium on VLSI Technology.

[24]  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).

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

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

[27]  Alessandro Calderoni,et al.  Physical Unbiased Generation of Random Numbers With Coupled Resistive Switching Devices , 2016, IEEE Transactions on Electron Devices.

[28]  Stefano Ambrogio,et al.  True Random Number Generation by Variability of Resistive Switching in Oxide-Based Devices , 2015, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[29]  B. DeSalvo,et al.  CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: Auditory (Cochlea) and visual (Retina) cognitive processing applications , 2012, 2012 International Electron Devices Meeting.

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

[31]  Z. Wei,et al.  True random number generator using current difference based on a fractional stochastic model in 40-nm embedded ReRAM , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[32]  Matthew Cook,et al.  Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..

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

[34]  Chiara Bartolozzi,et al.  Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems , 2014, Proceedings of the IEEE.

[35]  Themis Prodromakis,et al.  Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning , 2016, Front. Neurosci..

[36]  Ya-Chin King,et al.  A Contact-Resistive Random-Access-Memory-Based True Random Number Generator , 2012, IEEE Electron Device Letters.

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

[38]  Alessandro Calderoni,et al.  Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM , 2016, IEEE Transactions on Electron Devices.

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

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

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

[42]  A S Spinelli,et al.  Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity , 2017, Scientific Reports.

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

[44]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

[45]  Luping Shi,et al.  Enabling an Integrated Rate-temporal Learning Scheme on Memristor , 2014, Scientific Reports.

[46]  G. Indiveri,et al.  Neuromorphic architectures for spiking deep neural networks , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[47]  R. Williams,et al.  Sub-nanosecond switching of a tantalum oxide memristor , 2011, Nanotechnology.