Diverse spike-timing-dependent plasticity based on multilevel HfOx memristor for neuromorphic computing

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfOx/Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

[1]  Pinaki Mazumder,et al.  Online Supervised Learning for Hardware-Based Multilayer Spiking Neural Networks Through the Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Huaqiang Wu,et al.  Engineering interface-type resistance switching based on forming current compliance in ITO/Ga2O3:ITO/TiN resistance random access memory: Conduction mechanisms, temperature effects, and electrode influence , 2016 .

[3]  Wei D. Lu,et al.  Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks. , 2017, Nano letters.

[4]  Kristy A. Campbell,et al.  Pulse Shape and Timing Dependence on the Spike-Timing Dependent Plasticity Response of Ion-Conducting Memristors as Synapses , 2016, Front. Bioeng. Biotechnol..

[5]  P. Blaise,et al.  Prediction of semimetallic tetragonal Hf2O3 and Zr2O3 from first principles. , 2012, Physical review letters.

[6]  Xue-Bing Yin,et al.  Synaptic Metaplasticity Realized in Oxide Memristive Devices , 2016, Advanced materials.

[7]  H.-S. Philip Wong,et al.  Face classification using electronic synapses , 2017, Nature Communications.

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

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

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

[11]  X. Miao,et al.  Ultrafast Synaptic Events in a Chalcogenide Memristor , 2013, Scientific Reports.

[12]  D. Querlioz,et al.  Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices , 2013, IEEE Transactions on Nanotechnology.

[13]  Mahdi Kiani,et al.  Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs , 2015, Front. Neurosci..

[14]  Elisa Vianello,et al.  Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting , 2016, Front. Neurosci..

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

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

[17]  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.

[18]  B. Rajendran,et al.  Arbitrary Spike Time Dependent Plasticity (STDP) in Memristor by Analog Waveform Engineering , 2017, IEEE Electron Device Letters.

[19]  Yuchao Yang,et al.  Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity , 2015, Advanced materials.

[20]  F. Chiu.  A Review on Conduction Mechanisms in Dielectric Films , 2014 .

[21]  Wuhong Xue,et al.  Synaptic plasticity and learning behaviours in flexible artificial synapse based on polymer/viologen system , 2016 .

[22]  Farnood Merrikh-Bayat,et al.  Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors , 2015, Scientific Reports.

[23]  X. Miao,et al.  Associative Learning with Temporal Contiguity in a Memristive Circuit for Large‐Scale Neuromorphic Networks , 2015 .

[24]  Hyunsang Hwang,et al.  Organic core-sheath nanowire artificial synapses with femtojoule energy consumption , 2016, Science Advances.

[25]  E. V. Ivanova,et al.  XPS and cathodoluminescence studies of HfO2, Sc2O3 and (HfO2)1-x(Sc2O3)x films , 2013 .

[26]  Andrzej J. Kasinski,et al.  Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.

[27]  Byung-Gook Park,et al.  Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system , 2017, Nanotechnology.

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

[29]  Resistance Switching Characteristics Induced by O2 Plasma Treatment of an Indium Tin Oxide Film for Use as an Insulator in Resistive Random Access Memory. , 2017, ACS applied materials & interfaces.

[30]  Nicolas Locatelli,et al.  Learning through ferroelectric domain dynamics in solid-state synapses , 2017, Nature Communications.

[31]  Ji Rong Sun,et al.  Ternary Synaptic Plasticity Arising from Memdiode Behavior of TiOx Single Nanowires , 2016 .

[32]  Davood Shahrjerdi,et al.  A sub-1-volt analog metal oxide memristive-based synaptic device with large conductance change for energy-efficient spike-based computing systems , 2016, 1603.03979.

[33]  Doo Seok Jeong,et al.  Towards artificial neurons and synapses: a materials point of view , 2013 .

[34]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[35]  Haizhou Li,et al.  Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons , 2013, IEEE Transactions on Neural Networks and Learning Systems.

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

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

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

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