A Method of Determining Blocking Voltage of SiC MOSFET Based on the Neural Network Predict Model - Jun SHEN; Dong LIU; Maosen TANG; Xinglai GE; Junhan YE; Rongbin ZHOU (机车电传动)
Power device designers often need to design and optimize devices by device simulation, however, due to the simulation results are unknown before the simulation, designers need to gradually adjust the relevant parameters to make the simulation results constantly approach the target value, which costs much time. There is no effective solution to this problem. In order to solve the problem, an optimal prediction model based on neural network was proposed to determine the blocking voltage of SiC MOSFET. The test temperature, the first field limiting ring spacing, the ring spacing change step, the number of field limiting rings and the drift zone concentration were regarded as independent variable, and brought into the device simulation software for simulation. The blocking voltage of VDMOSFET was obtained as the dependent variable, which was brought into BP neural network and RBF neural network for prediction respectively, and the prediction errors of the two were compared. The results show that the BP neural network prediction model by using LM algorithm can well predict the forward blocking voltage of VDMOSFET,and save a lot of time for designers.