On Neural Networks Based Electrothermal Modeling of GaN Devices

This paper presents an efficient artificial neural network (ANN) electrothermal modeling approach applied to GaN devices. The proposed method is based on decomposing the device nonlinearity into intrinsic trapping-induced and thermal-induced nonlinearities that can be simulated by low-order ANN models. The ANN models are then interconnected in the physics-relevant equivalent circuit to accurately simulate the transistor. Genetic algorithm (GA)-based training procedure has been implemented to find optimal values for the weights of the ANN models. The modeling approach is used to develop a large-signal model for a 1-mm gate-width GaN high-electron mobility transistor (HMET). The model has been implemented in the advanced design system (ADS) and it has been validated by pulsed and continues small- and large-signal measurements. The model simulations showed a very good agreement with the measurements and verify the validity of the developed technique for dynamic electrothermal modeling of active devices.