Large-Signal Modeling of GaN HEMTs Using Hybrid GA-ANN, PSO-SVR, and GPR-Based Approaches

This article presents an extensive study and demonstration of efficient electrothermal large-signal GaN HEMT modeling approaches based on combined techniques Genetic Algorithm (GA) with Artificial Neural Networks (ANN), Particle Swarm optimization (PSO) Support Vector Regression (SVR). Another promising Gaussian Process (GPR) approach is also explored presented. The GA-ANN addresses the typical problem local minima associated backpropagation (BP) ANN. GA successfully aids in determination optimal initial values for BP-ANN enables it to find a unique solution after subsequent iterations higher rate convergence. achieved using PSO-SVR lower variables. developed are demonstrated used simulate gate drain currents 2-mm device. All models relatively simple, practical, easy implement. embedded equivalent circuit's model built Advanced Design System (ADS) software. implemented validated by measurements very good fitting results have been obtained. showed accurate simulation nonlinear power amplifier computational speed