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 physicsbased 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-tocycle, as well as device-to-device variations. The significance of integrating variability into RRAM models is underscored using a sensing example. Index Terms-Resistive RAM (RRAM), physics-based models, cycle-to-cycle variability, device-to-device variability, Stanford model, memristor, sense amplifier, resistive-switching, 1T-1R there cannot be a proper assessment of the functionality and yield of RRAM-based ICs, which will result in a series of expensive trial-and-errors. Pessimistic design approaches which allocate huge safety margins to accommodate variability are not recommended since they sacrifice design properties like energy, delay, and area. Consequently, there is an exigent RRAM models FEM DFT KMC Simulation speed Stanford-PKU model Classification based on abstraction levels Computational cost Physical detail Circuit µm 2-mm 2 Devicẽ 10 3 nm 3 Material few nm 3 Compact model Classification based on modeling approach Physical model (from first principles) Analytical Physics-based Black-box (measurement) Stanford-PKU model

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