Utilizing multiple state variables to improve the dynamic range of analog switching in a memristor

Abstract

Memristors and memristive systems have been extensively studied for data storage and computing applications such as neuromorphic systems. To act as synapses in neuromorphic systems, the memristor needs to exhibit analog resistive switching (RS) behavior with incremental conductance change. In this study, we show that the dynamic range of the analog RS behavior can be significantly enhanced in a tantalum-oxide-based memristor. By controlling different state variables enabled by different physical effects during the RS process, the gradual filament expansion stage can be selectively enhanced without strongly affecting the abrupt filament length growth stage. Detailed physics-based modeling further verified the observed experimental effects and revealed the roles of oxygen vacancy drift and diffusion processes, and how the diffusion process can be selectively enhanced during the filament expansion stage. These findings lead to more desirable and reliable memristor behaviors for analog computing applications. Additionally, the ability to selectively control different internal physical processes demonstrated in the current study provides guidance for continued device optimization of memristor devices in general.