Behavioral Modeling of SiC MOSFET Static and Dynamic Characteristics Based on Particle Swarm Optimization Algorithm

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have been widely applied in electronic equipment, owing to the rapidly switching speed and superior thermal performance. Under high-frequency switching, the parasitic parameters in MOSFET driving circuit and power circuit will cause problems such as voltage overshoot and oscillation, which limit the application of power devices. To improve the stability and reliability of electronic circuit, considering that the operating frequency of the MOSFET reaches several hundred kHz, an accurate high-frequency model of the SiC MOSFET should be established, including static and dynamic models. This study proposes a SiC MOSFET behavioral model with parasitic parameters based on the particle swarm optimization (PSO) algorithm. The PSO algorithm is used to extract the parameters of the improved Enz-Krummnacher-Vittoz model and a static characteristic behavioral model of the SiC MOSFET is established, avoiding a large amount of simulation or calculation time for circuit and datasheet-drive models, the efficiency is increased by 60%, and the model accuracy is 98% compared to the datasheet. On the basis of the static characteristics, a nonlinear capacitance model is established using the PSO algorithm, and the parasitic inductance of the MOSFET is extracted through a finite element analysis. The fourth-order Runge-Kutta method is employed to solve the state equation, and the dynamic characteristic behavioral model of the MOSFET is established based on the static characteristics, parasitic capacitance and inductance, device and circuit parameters, which is simpler and easier to implement than the physical modeling method. A pulse testing experimental circuit is constructed to validate the accuracy of the dynamic model, compared with the sample model and experimental results, the errors of the behavioral model are less than 3%. This study provides valuable insights for MOSFET modeling and optimization, contributing to the dynamic analysis and reliability research of electronic circuits.