GEM: A Generalized Memristor Device Modeling Framework Based on Neural Network for Transient Circuit Simulation
Yuhang Zhang, Guanghui He, Kea‐Tiong Tang, Yongfu Li, Guoxing Wang
Abstract
Conventional physics-based memristor device modeling methods highly rely on human expertise, which results in a long development period. To address the aforementioned challenges, we propose a new generalized memristor (GEM) device modeling framework based on the artificial neural network (ANN) technique, which has a minimum dependency on the underlying physics, resulting in a fast turn-around development time for customized memristor devices. GEM framework models the switching and conducting behaviors of the memristor devices separately, avoiding the signal-dependence issue in the prior time-series data modeling method. The result of the GEM framework is a compact model that supports general-purpose circuit simulators. Experimental results show that our compact model achieves a ratio of root-mean-square error to peak-to-peak (RMSE/PP) of 3.6% compared to the physics-based device model. Performance analysis of memristor-based logic and memristor crossbar circuits are conducted to demonstrate the effectiveness of our proposed GEM framework for the design and analysis of memristor-based circuits.