Analog-Type Resistive Switching Devices for Neuromorphic Computing

Brain-inspired neuromorphic computing has attracted considerable attention due to its potential to circumvent the von Neumann bottleneck and mimic human brain activity in electronic systems. The key to developing high-performance and energy-efficient neuromorphic computing systems lies in the realization of electronic devices that can closely mimic biological synapses. Resistive random-access memory (RRAM) has shown some important properties for implementing synaptic functions, including analog weight storage and analog switching. Herein, the recent progress in analog-type RRAM is reviewed. The mechanisms underlying the analog switching behavior in RRAM and different types of synaptic plasticity based on the analog switching behavior are discussed. Methods to improve the analog switching behavior and synaptic plasticity are then illustrated. Finally, a summary and a perspective on future research are presented.