Perovskite-Nanowire-Array-Based Continuous-State Programmable Artificial Synapse for Neuromorphic Computing

Abstract

Perovskite-based memristors with tunable nonvolatile states are developed to mimic the synaptic interconnects of biological nervous systems and map neuromorphic computing networks to integrated circuits. To emulate the plasticity of synaptic structures, memristors with robust multilevel resistive states are fabricated in this work using high-density polycrystalline MAPbCl3 nanowires (NWs) array that vertically integrated using solution method. In particular, the fabricated memristors exhibit both short- and long-term plasticity and traits akin to biological synapses. A fabricated memristor device is precisely programmed to 18 resistive states and each state exhibits stable data retention of more than 100 000 s. Furthermore, a matrix processing unit using a 4-by-4 memristor array is fabricated as the hardware core of an encoder–decoder artificial neural network to demonstrate high accuracy and reliable in-image font conversion. The resistive states of the 16 memristors are precisely programmed to the corresponding resistance values for specific synaptic weights of the artificial-neural-network-trained offline. In addition, experimental characterization and first-principles simulations attribute the continuous programmability and high reliability features of the memristors to the confinement mechanisms of the horizontal grain-boundary structure in polycrystalline perovskite NWs.