LiSiO_X-Based Analog Memristive Synapse for Neuromorphic Computing

Abstract

The progress in the neuromorphic computing hinges on the development of nanoscale analog artificial synapses. Here, we report a LiSiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</sub> (LSO)-based memristive synapse with 100-level conductance states under identical pulses, representing synaptic potentiation and depression behaviors. The superior analog behaviors originate from the dynamic evolution of an electro-thermal modulation region with the motion of lithium and oxygen ions. A three-layer perceptron was constructed in simulation with LSO synapses, and a 91.97% recognition accuracy was achieved for handwritten digits. Moreover, the influences of several critical parameters, including device variability and weight precision, on the accuracy have been investigated. This letter provides guidelines for the optimization of synaptic device in robust memristive neural network.