Nonlinear compact thermal modeling of self-adaptability for GaN high-electron-mobility-transistors using Gaussian process predictor and ensemble Kalman filter

Thermal issue has been regarded as one of the bottlenecks for GaN high-electron-mobility transistor (HEMT) performance and reliability, which highlights the importance of accurate thermal modeling. In the present work, we propose a GP (Gaussian process)-resistor–capacitor compact thermal model integrated with the ensemble Kalman filter (EnKF) to handle the nonlinear problems attributed to the temperature-dependent properties of GaN HEMTs under large-signal working conditions. The GP predictor is employed for the nonlinear correction term, with strong ability and extendibility to characterize various temperature-dependent relations resulting from different design configurations and materials. The model is identified via the EnKFs by inputting a sequence of channel temperature oscillations induced by imposing a large-signal continuous wave heating source to the device. Furthermore, an adaptation mode is devised for the in situ and timely update of the model parameters to adapt to the thermal variability of GaN devices, avoiding storing a large amount of historical data and repeated offline regressions. The validation of our modeling scheme is conducted through the case study on GaN-on-SiC HEMT’s detailed 3D finite element method simulations.