Thermal switching of TiO2-based RRAM for parameter extraction and neuromorphic engineering

Recently, resistive switching random access memory (RRAM) has gained maturity for storage class memory and inmemory 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 TiO2-based RRAM devices. Thermal switching is explained by defect rediffusion controlled by the activation energy for defect migration in TiO2 . 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.