Hybrid Data-Driven Modeling Methodology for Fast and Accurate Transient Simulation of SiC MOSFETs

Abstract

To enable fast and accurate models of SiC MOSFETs for transient simulation, a hybrid data-driven modeling methodology of SiC MOSFETs is proposed. Unlike conventional modeling methods that are based on complex nonlinear equations, data-driven artificial neural networks (ANNs) are used in this article. For model accuracy, the <inline-formula><tex-math notation= LaTeX >$I$</tex-math></inline-formula>–<inline-formula><tex-math notation= LaTeX >$V$</tex-math></inline-formula> characteristics are measured in the whole operation region to train the ANN. The ANN model is then combined with behavior-based equations to model the cutoff region and to avoid overfitting the ANN. In addition, the <inline-formula><tex-math notation= LaTeX >$C$</tex-math></inline-formula>–<inline-formula><tex-math notation= LaTeX >$V$</tex-math></inline-formula> characteristics are modeled by ANNs with a logarithmic scale for accuracy. The proposed model is implemented and simulated in SPICE simulator SIMetrix. The simulation results are compared with experimental results from a double pulse tester to validate the proposed modeling methodology. The model is also compared with the Angelov model created by the Keysight MOSFET modeling software. The comparison results show that the proposed model is more accurate than the Angelov model. Besides, when compared to the Angelov model, the proposed model requires 30% less computation time when simulating a double pulse tester. In addition, the proposed modeling method also has better adaptability to model different types of SiC MOSFETs.