Hybrid small-signal model parameter extraction for GaN HEMT based on QGA

A novel algorithm is proposed for optimal extraction of GaN HEMT small-signal model parameters. The proposed Quantum Genetic Algorithm (QGA) exploits the superposition, entanglement and interference of quantum states, which solves the problems of high number of iterations and slow convergence when obtaining optimal solutions using Genetic Algorithms (GA). Meanwhile, it is solved that the Particle Swarm Optimisation (PSO) algorithm produces premature convergence and easily falls into the local optimum solution. In order to avoid the influence of distributed parasitic effects in large size devices under high-frequency conditions, a suitable frequency range is determined and combined with direct extraction techniques to determine the range of parameter values. The model parameter values are optimised step by step using QGA. In order to verify the superiority of QGA, QGA and PSO algorithms are both used to optimise GaN HEMT small-signal model parameters. By comparing the modelled S-parameter effects of the QGA and the PSO algorithm, it can be found that the QGA has better consistency with the measured data.