Incorporating Variability of Resistive RAM in Circuit Simulations Using the Stanford-PKU Model

Intrinsic variability observed in resistive-switching devices (cycle-to-cycle and device-to-device) is widely recognised as a major hurdle for widespread adoption of Resistive RAM technology. While physics-based models have been developed to accurately reproduce the resistive-switching behaviour, reproducing the observed variability behavior of a specific RRAM has not been studied. Without a properly fitted variability in the model, the simulation error introduced at the device-level propagates through circuit-level to system-level simulations in an unpredictable manner. In this work, we propose an algorithm to fit a certain amount of variability to an existing physics-based analytical model (Stanford-PKU model). The extent of variability exhibited by the device is fitted to the model in a manner agnostic to the cause of variability. Further, the model is modified to better reproduce the variations observed in a device. The model, fitted with variability can well reproduce cycle-to-cycle, as well as device-to-device variations. The significance of integrating variability into RRAM models is underscored using a sensing example.