TCAD-enabled machine learning framework for DC and RF performance evaluation of InGaAs sub-channel DG-HEMTs

Abstract

This research presents a machine learning (ML)-based model that determines the DC and RF characteristics of InGaAs sub-channel double gate high electron mobility transistors (DG-HEMTs) to optimize the device structure. We employ technology computer-aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub-channel DG-HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5-fold cross-validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods.