Defective Engineering Tuning the Analog Switching Linearity and Symmetry of Two‐Terminal Artificial Synapse for Neuromorphic Systems

Abstract

AbstractNeuromorphic computing inspired by memristors has gained considerable attention due to its low power and easy integration. However, state‐of‐the‐art two‐terminal resistive switching memristors based on conductive filament formation suffer from high variability and poor controllability. As a three‐terminal device operated through electrochemistry and dynamic insertion/extraction of ions, the electrochemical ion synapse demonstrates deterministic control of electron conductivity based on ion doping. But, integrating the electrochemical ion synapse into crossbar arrays will pose higher challenges and lower integration density. Herein, inspired by first‐principles calculations, a two‐terminal bidirectional plasticity electrochemical artificial synapse with integrated lithium polymer electrolyte and polycrystalline tungsten oxide layer is reported. The linearity and stability of the device weight update are greatly improved by adjusting the defect concentration of the polycrystalline WO3 layer. Even after 16 000 write‐read events in the air, its performance remained almost unchanged. Moreover, it has an over‐limit protection mechanism under one‐way stimulation that exceeds the normal range. Based on this excellent stability, the authors designed and successfully simulated the “muscle memory” that the programmatical organization of the nervous system leads to proficiency in specific actions.