Small-Signal Modeling of GaN HEMTs for mm-Wave Applications

At high frequencies, the Gallium Nitride High Electron Mobility Transistor (GaN HEMT) is one of the best devices for handling high temperatures and frequencies, making it a strong candidate for future wireless communication systems. However, at high frequencies, additional parasitic elements significantly impact performance, necessitating a distributed-element equivalent circuit to account for all parasitic parameters. In this paper, a 22-element model is adopted to characterize the intrinsic transistor along with all expected distributed parasitic elements. A hybrid direct-optimization parameter extraction procedure is developed, implemented, and applied to GaN HEMTs on a diamond substrate of different sizes. Five optimization algorithms are demonstrated, including Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), the Sine-Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and the Genetic Algorithm (GA). The model's accuracy is validated by matching S-parameter measurements up to 40 GHz, along with the physical verification of the extracted values. The results show high accuracy and confirm that the developed model is suitable for reliable design of application circuits.