Degradation Prediction of GaN HEMTs under Hot-Electron Stress Based on ML-TCAD Approach

Abstract

In this paper, a novel approach that combines technology computer-aided design (TCAD) simulation and machine learning (ML) techniques is demonstrated to assist the analysis of the performance degradation of GaN HEMTs under hot-electron stress. TCAD is used to simulate the statistical effect of hot-electron-induced, electrically active defects on device performance, while the artificial neural network (ANN) algorithm is tested for reproducing the simulation results. The results show that the ML-TCAD approach can not only rapidly obtain the performance degradation of GaN HEMTs, but can accurately predict the progressive failure under the work conditions with a mean squared error (MSE) of 0.2, informing the possibility of quantitative failure data analysis and rapid defect extraction via the ML-TCAD approach.