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. Copyright © 2014 John Wiley & Sons, Ltd.