Computational Model of Ta2O5/TaOx Memristors: Predicting Resistive Switching Behavior and Filament Growth Dynamics for Enhanced Device Control and Optimization

Memristors have been suggested for various applications, including nonvolatile memory and neuromorphic systems. In contrast to traditional devices that rely purely on electron transport, memristors use resistive switching (RS), which is based on the redistribution of ions. Despite multiple experimental and modeling efforts to investigate the RS process, a full physical framework that can quantitatively explain the dynamic RS behavior remains difficult. A model that quantitatively outlines dynamic resistive switching, including set/reset cycling and retaining self-consistency from the initial formation process, has yet to be developed. This study presents a $\text{TazO}_{5}/\text{TaO}_{\mathrm{X}}$ device model that can accurately predict all crucial RS properties during the formation process and subsequent set and reset cycles. The simulation outcomes were also contrasted with experimental DC and pulse measurements in 1R structures and revealed excellent agreement.