Simplified Silicon Carbide MOSFET Model Based on Neural Network

Abstract:

Silicon carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) has become the core device of power-switching converters with its excellent characteristics. An accurate and simple model of medium voltage SiC MOSFET is necessary for device evaluation, system design, and power converter efficiency prediction. This paper presents an equivalent circuit simulation behavior model, of which all parameters are abstracted from the datasheet, based on a three layers’ artificial neural network. The inputs are the gate voltage and the drain-source voltage, and the output is the drain current. In addition, the method of importing the neural network into circuit simulation software Pspice is discussed. The model is simple to create with no additional experiment, and is suitable for engineers at all stages to evaluate SiC MOSFET characteristics. The accuracy of the new model proposed in this paper has been verified by comparison with the data in the datasheet.

• Periodical: Materials Science Forum (Volume 954)
• Pages: 163-169
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.954.163
• Online since: May 2019
• Authors: Yu Hao Lee, Mu Zhang, Ping Juan Niu*, Ping Fan Ning, Lei Liu, Shu Shu Lee
• Keywords: Modeling, Neural Network, Power MOSFET, Pspice, Silicon Carbide (SiC), Static Characters

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