Quantitative, Dynamic TaOx Memristor/Resistive Random Access Memory Model

Abstract

Oxide-based memristors are two-terminal devices whose resistance can be modulated by the history of applied stimulation. Memristors have been extensively studied as memory (as resistive random access memory) and synaptic devices for neuromorphic computing applications. Understanding the internal dynamics of memristors is essential for continued device optimization and large-scale implementation. However, a model that can quantitatively describe the dynamic resistive switching (RS, e.g., set/reset cycling) behavior in a self-consistent manner, starting from the initial forming process, is still missing. In this work, we present a Ta2O5/TaOx device model that can reliably predict all key RS properties during forming and repeated set and reset cycles. Our model revealed that the forming process originates from electric field focusing and localized heating effects from the initial nonuniform oxygen vacancy (V-O) defect distribution. A broad range of device behaviors, including cycling of the V-O distribution during set/reset cycles, multilevel storage, and two different filament growth processes, can be quantitatively captured by the model. In particular, a bulk-type doping effect with low programming current was found to produce linear conductance changes with a large dynamic range that can be highly desirable for neuromorphic computing applications. The simulation results were also compared with experimental dc and pulse measurements in 1R and 1T1R structures and showed excellent agreements.