A High-Efficiency Switching Current Model for Power GaN HEMT Based on High-Order Time-Derivative Nonlinear Current Components and Advanced Neural Network Algorithms

GaN HEMTs are gaining widespread use in power electronics due to their excellent power and frequency characteristics. Accurate and efficient device models are essential for power circuit design and simulation. This article presents a novel approach to model construction, based on current components by their time derivatives. It incorporates advanced static and recurrent neural network concepts to design a precise model architecture. The proposed model integrates the effects of self-heating and couples three distinct current components: a zeroth-order time-derivative intrinsic current module, a first-order time-derivative capacitive current module, and a high-order time-derivative nonideal current module, resulting in a comprehensive GaN HEMT nonlinear dynamic switching output current model. Each model module is trained and validated using experimental measurements. The overall coupled model is further validated through experimental data, and its application is demonstrated in a circuit simulation of a standard double-pulse test with an inductive load. Finally, the proposed model is comprehensively evaluated against conventional models across various metrics, including time-frequency domain accuracy, time-step precision, computational efficiency, and generalization, highlighting the significant advantages of our approach.