A Generalized Workflow for Creating Machine Learning-Powered Compact Models for Multi-State Devices

Abstract

The predictive capability of existing physical descriptions of multi-state devices ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., oxide memristors, ferroelectrics, antiferroelectric, etc.) cannot be fully leveraged in circuit simulations due to practical limitations regarding the complexity of compact models. We attempt to circumvent this issue by adopting a machine-learning (ML) - based approach to develop a compact model that retains the full physical description of these devices. ML-based modeling approaches have garnered immense interest in recent years and have already been successfully utilized in making models for several novel devices. A known hurdle for ML-based compact modeling is the need for large amount of experimental data to properly train the model. We propose a method to simulate additional data by duplicating the data and adding Gaussian Noise to the duplicates. We propose a generalized framework to - (i) facilitate efficient training of ML-based device models, (ii) conduct seamless conversion to Verilog-A model, and (iii) interface with industry-standard circuit simulators (HSPICE, SPECTRE, etc.). We demonstrate the capabilities of our framework using the hafnium oxide (HfOx) memristor as a test device. As the source of the training data, we use a physical model that unifies detailed atomic-level descriptions with self-consistent evaluation of electronic transport. In addition, we test our model with experimental data for multiple memristor samples and repeated cycles of the same sample. Our ML-based framework prepares a circuit-compatible compact model to facilitate system-level simulations. With our model, we achieve a root mean squared error (RMSE) of 0.000863 and an R2 of 0.977371 on our testing data.