Novel DPT-Based SPICE Modeling Approach for Enhanced Prediction of Parallel SiC MOSFET Switching Dynamics

This work presents a novel SPICE modeling methodology for accurately predicting the dynamic switching behavior of parallel-connected Silicon Carbide (SiC) MOSFETs. In high-power converter applications, parametric dispersion among nominally identical SiC devices-such as variations in gate threshold voltage, transconductance and conduction resistance-poses a significant challenge to balanced current sharing and thermal balance. By leveraging Double Pulse Test (DPT) experimental data, the proposed approach addresses the critical issue of parametric dispersion in SiC devices by enabling the emulation of individual device behavior under transient switching conditions, which is essential for designing reliable high-power converters. The proposed technique does not require modification of the internal SPICE model of the MOSFET, which is often encrypted or vendor-specific, instead, external gate-driving parameters are selectively tuned to emulate the dynamic response of each device. This preserves model integrity while significantly improving simulation accuracy, supporting a wide range of application domains, including electric vehicles, renewable energy systems, and industrial power electronics. The proposed methodology enhances the design of robust parallel-connected systems, ensuring improved current sharing and reduced thermal stress.