Traditional Technology Computer-Aided Design (TCAD) simulations face significant challenges in meeting the demands of high-throughput device design, particularly for wide-bandgap semiconductor devices such as GaN transistors, where numerical convergence issues and high computational costs are prevalent. To address these limitations, this work proposes a stacked regression–based TCAD–machine learning (TCAD-ML) framework for efficient and accurate prediction of the electrical characteristics of GaN heterojunction field-effect transistors (HFETs). The framework integrates a fully connected neural network and an XGBoost model as base learners, with a Random Forest meta-learner to exploit complementary latent features. Using only 480 training samples generated from calibrated TCAD simulations, the proposed model achieves high prediction accuracy for both transfer and output characteristics, with R2 values exceeding 0.999. Furthermore, a successive approximation strategy is introduced to mitigate the impact of TCAD non-convergence in certain design regions, enabling reliable prediction in parameter spaces inaccessible to conventional simulations. Compared with existing machine learning–based device models, the proposed approach demonstrates superior accuracy, strong few-shot learning capability, and excellent generalization performance. These results highlight the potential of stacked regression–based TCAD-ML frameworks as an effective alternative to traditional TCAD for accelerating GaN transistor design and optimization.
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