Compensated Neural Network Training Algorithm with Minimized Training Dataset for Modeling the Switching Transients of SiC MOSFETs

Accurate modeling of the switching transients of SiC MOSFETs is essential for overvoltage evaluation, EMI prediction, and other critical applications. Due to the fast switching speed, the switching transients of SiC MOSFETs are highly sensitive to parasitic parameters and nonlinear components, making precise modeling challenging. This paper proposes a hybrid model for SiC MOSFET, in which the analytical model is treated as the basis to provide the fundamental waveforms (knowledge-driven), while the neural network (NN) is utilized to fit the high-order and nonlinear features (data-driven). An NN training method with augmented data is proposed to minimize the training datasets. Verification results show that, even though the NN is trained with the data from a single operating condition, the model can accurately predict switching transients of other operating conditions. The proposed methodology has the potential to co-work with the “black-box” or “grey-box” models to enhance the model accuracy.