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Forming-Less Compliance-Free Multistate Memristors as Synaptic Connections for Brain-Inspired Computing
journal contribution
posted on 2020-03-06, 19:06 authored by Sien Ng, Rohit Abraham John, Jing-ting Yang, Nripan MathewsHardware realization
of artificial neural networks (ANNs) requires
analogue weights to be encoded into the device conductances via blind
update and access operations, leveraging Kirchhoff’s circuit
laws. However, most memristive solutions lag behind in this aspect
due to numerous device nonidealities, like limited number of addressable
states, need for a stringent compliance current control, and an electroforming
process. By modulating the oxygen vacancy profile of tin oxide switching
elements, here we design and evaluate multistate memristors as synaptic
connections for brain-inspired computing. Harnessing the advantages
of a forming-less compliance-free operation, our devices display gradual
switching transitions across multiple conductance states, sufficing
the switching requirements of synaptic connections in an ANN. The
soft boundary conditions are analyzed systematically, and spike-based
plasticity rules, state-dependent spike-timing-dependent-plasticity
(STDP) modulations, ternary digital logic, and analogue updatability
schemes are proposed and demonstrated comprehensively to establish
the analogue programming window of our memristors.