Neural approach for temperature-dependent modeling of GaN HEMTs

Gallium nitride high electron-mobility transistors have gained much interest for high-power and high-temperature applications at high frequencies. Therefore, there is a need to have the dependence on the temperature included in their models. To meet this challenge, the present study presents a neural approach for extracting a multi-bias model of a gallium nitride high electron-mobility transistors including the dependence on the ambient temperature. Accuracy of the developed model is verified by comparing modeling results with measurements.