Uncertainty-Aware Gate-Lifetime Prediction of p-GaN Gate HEMTs Using Gaussian Processes

Abstract

The accurate prediction of Gallium Nitride High-Electron Mobility Transistors (GaN HEMTs) lifetime is essential for ensuring the reliability of power electronics. However, the complex and often competing degradation mechanisms within a single GaN-based transistor make lifetime extrapolation particularly challenging, especially under limited-data scenarios. In this work, we explore two machine learning approaches, i.e., XGBoost Regression and Gaussian Process Regression (GPR), for static gate lifetime prediction based on early measurements of current and ON-state resistance. In particular, we use features derived from empirical models to improve accuracy and model-specific methods to estimate uncertainty. We compare bootstrapped XGBoost ensembles, which yield empirical confidence intervals, with GPR, which provides analytical uncertainty estimates. Experiments on a time-dependent gate breakdown (TDGB) dataset spanning 16 voltage–temperature combinations show that GPR achieves an SMAPE of 8.8% and ECE of 0.028, outperforming XGBoost in Leave-One-Condition-Out Cross-Validation. These results highlight the feasibility of our proposed uncertainty-aware gate-lifetime prediction for Schottky p-GaN gate HEMTs in small-sample settings, and provide a basis for extending the framework towards time-dependent degradation modeling.