Physics-based modeling of volatile resistive switching memory (RRAM) for crosspoint selector and neuromorphic computing

Volatile resistive switching memory (RRAM) is raising strong interest as potential selector device in crosspoint memory and short-term synapse in neuromorphic computing. To enable the design and simulation of memory and computing circuits with volatile RRAM, compact models are essential. To fill this gap, we present here a novel physics-based analytical model for volatile RRAM based on a detailed study of the switching process by molecular dynamics (MD) and finite-difference method (FDM). The analytical model captures all essential phenomena of volatile RRAM, e.g., threshold/holding voltages, on-off ratio, and size-dependent retention. The model is validated by extensive comparison with data from Ag/SiO_x RRAM. To support the circuit-level capability of the model, we show simulations of crosspoint arrays and neuromorphic time-correlated learning.