Characteristics prediction on SiC MOSFET implemented with BP neural network

Duo to the wide band gaps, fast saturated electron drift velocities and high breakdown electric field strength of the silicon carbide (SiC), it is an appropriate candidate to develop power supplies working in high temperature and high voltage environments. Usually, the requirement on the reliability of these devices is much higher than those of the universal power supplies. Based on the real device structure of the 4H-SiC MOSFET, the output characteristic is simulated with TCAD package and verified by comparing with the testing results from the datasheet, which provides the data set for training BP neural network. Furthermore, an BP neural network is trained to predict the output characteristics of the MOSFET. Agreement between the predicted characteristics and real characteristics is achieved. The trained neural network can be easily integrated in embedded system and provides the possibility for health monitoring and fault diagnosis based on artificial intelligence.