On hybrid model parameter extraction of GaN HEMTs based on GA, PSO, and ABC optimization

This paper presents a comparison between different optimization techniques in the context of hybrid small-signal model (SSM) parameter extraction for GaN High Electron Mobility Transistors (HEMTs). The optimization techniques considered in this work are: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The algorithms were tested for their robustness, convergence speed, and efficiency. PSO algorithm was shown to be the most suitable in terms of robustness and efficiency. However, all techniques were able to obtain credible model parameters, which confirms the reliability of the adopted procedure. The quality of extraction was evaluated by means of S-parameter fitting at different bias conditions.