A Physics-Informed Recurrent Neural Network for RRAM Modeling

Abstract

Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to enhance the modeling capabilities. The integration strategy of Verilog-A equivalent circuits, is also developed for compatibility with existing general-purpose circuit simulators. The Verilog-A model can be easily adopted into the SPICE-type simulator for the circuit design with a variable step that differs from the training process. Numerical experiments with real RRAM devices data demonstrate the feasibility and advantages of the proposed methodology.

A Compact Memristor Model Based on Physics-Informed Neural Networks

“…However, it has the disadvantage of having to consider past time and is more complex than the traditional multi-layer perceptron (MLP) model [27]. Moreover, the study on memristor-based neural networks utilizing recurrent neural network (RNN) [28] demonstrates excellent performance in data prediction. Nonetheless, the structure of the neural network is highly complex.…”