Characteristics prediction on SiC MOSFET implemented with BP neural network

Jiuxu Song; Yaoshuai Yang

doi:10.1117/12.2689736

10 August 2023 Characteristics prediction on SiC MOSFET implemented with BP neural network

Jiuxu Song, Yaoshuai Yang

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482B (2023) https://doi.org/10.1117/12.2689736
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

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.

Citation Download Citation

Jiuxu Song and Yaoshuai Yang "Characteristics prediction on SiC MOSFET implemented with BP neural network", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482B (10 August 2023); https://doi.org/10.1117/12.2689736

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