Integrated Spatiotemporal Multiscale- Multiphysics-Uncertainty Simulation for Controlling Variability in RRAM Devices

Abstract

Resistive random access memory (RRAM) is a leading candidate for next-generation nonvolatile memory and neuromorphic computing. However, its performance is limited by inherent switching variability and uncertainties in spatiotemporal multiscale materials and processes. This study integrates multiphysics and multiscale modeling with uncertainty quantification (UQ) to systematically address these limitations and reduce uncertainties. UQ identifies critical inputs that govern key performance metrics, including theON/OFF ratio, forming voltage, and power consumption, reducing their statistical distributions with the probabilities of reliability analysis over 92%. The phase field model (PFM) captures the morphological evolution of conductive filament (CF) and, by incorporating a second-order time derivative for ion diffusion, reveals the impact of morphological fluctuations governing RRAM behavior. Drift diffusion simulations further demonstrate that bilayer structures confine CF fractures to the HfO2 layer through interfacial constraints. This modeling framework provides a systematic approach to mitigate variability and improve the design and reliability of RRAM devices.