Physics-Informed Learning for Versatile RRAM Reset and Retention Simulation

Abstract

Resistive random-access memory (RRAM) constitutes an emerging and promising platform for compute-inmemory (CIM) edge AI. However, the switching mechanism and controllability of RRAM are still under debate owing to the influence of multiphysics. Although physics-informed neural networks (PINNs) are successful in achieving mesh-free multiphysics solutions in many applications, the resultant accuracy is not satisfactory in RRAM analyses. This work investigates the characteristics of RRAM devices - retention and reset transition which are described in terms of the dissolution of a conductive filament (CF) in 3-D axis-symmetric geometry. Specifically, we provide a novel neural network characterization of ion migration, Joule heating, and carrier transport, governed by the solutions of partial differential equations (PDEs). Motivated by physics-informed learning, the separation of variables (SOV) method and the neural tangent kernel (NTK) theory, we propose a customized 3-channel fully-connected network and a modified random Fourier feature (mRFF) embedding strategy to capture multiscale properties and appropriate frequency features of the self-consistent multiphysics solutions. The proposed model eliminates the need for grid meshing and temporal iterations widely used in RRAM analysis. Experiments then confirm its superior accuracy over competing physics-informed methods.