Thermal switching of $\text{TiO}_{2}$ -based RRAM for parameter extraction and neuromorphic engineering

Abstract

Recently, resistive switching random access memory (RRAM) has gained maturity for storage class memory and in-memory computing. For these applications, an improved control of the switching phenomena can lead to higher data density and computing accuracy, thus paving the way for RRAM-based artificial intelligence (AI) accelerators for edge computing. This work presents a study of thermally-induced switching in $\text{TiO}_{2}$ -based RRAM devices. Thermal switching is explained by defect rediffusion controlled by the activation energy for defect migration in $\text{TiO}_{2}$. Experiments and simulations support thermal switching as a tool for parameter extraction in RRAM, as well as for novel neuromorphic cognitive functions for brain-inspired computing.