An Automated Model Tuning Procedure for Optimizing Prediction of Transient and Dispersive Behavior in Wide Bandgap Semiconductor FETs

This article presents an automated procedure for developing wide bandgap semiconductor device models capable of capturing high-frequency effects in applications relevant to next generation power electronics. A derivative-free global optimization algorithm is used to autonomously model both the static and dynamic behavior of a power semiconductor device. This procedure is demonstrated with a specific model, but the procedure is model-agnostic and not exclusive to this implementation. The modeling procedure creates a compact and modular power electronic device model accounting for both frequency-dependence of device inter-electrode capacitances and characterization uncertainty of device parasitic package inductances. The procedure targets prediction of specific signal features in the transient device behavior. Digital twin development and validation are performed under disparate switching conditions to demonstrate the overall improvement in model predictive power. The proposed modeling approach serves to reduce manual model development time, while improving time-domain prediction under switching conditions of interest to application engineers and systems integrators. This is particularly important in the case of wide bandgap semiconductors, which pose challenges for conventional device models due to their fast switching dynamics. The effectiveness of the presented method is demonstrated by automatically generating a device model for a 1.2-kV SiC mosfet and comparing predicted switching behavior with empirical double pulse test behavior.