Knowledge Transfer for GaN HEMTs Parameter Extraction Based on Hybrid Model

Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) offer advantages such as wide bandgap and high electron mobility. Design of a circuit with GaN requires a highly accurate equivalent circuit model, which requires a computationally expensive parameter extraction process. Despite the success of the optimization-based parameter extraction methods, they are generally designed to work for individual cases, leading to inferior performance. To resolve this challenge, we harness modern AI techniques to significantly boost this cum-bersome process. More specifically, we enhance the conventional optimization-based method with three novel modifications: steps: (1) data-driven calibration for classic initialization methods with empirical equations; (2) adaptive search space for refining search space with faster searching speed and more accurate solution; (3) extracted parameters embedding using deep kernel learning for higher accuracy. The experimental results show that our proposed method reduces the optimization time by 5 ×and has a 1.18 × accuracy improvement compared to competing methods.