Demonstration of accurate ID-VG characteristics modeling in SiC mosfets using separated artificial neural networks with small training dataset

Abstract

Abstract This study developed a novel approach based on separated artificial neural networks (ANNs) to efficiently and accurately model the drain current (I D )–gate voltage (V G ) characteristics of silicon carbide (SiC) power MOSFETs efficiently and accurately. We found that a single ANN cannot model the entire I D –V G range under a large ON/OFF current ratio (10− 12 to 10− 1 mA/mm), which is often observed in wide-bandgap semiconductor technologies, such SiC MOSFETs. To address this problem, we developed a method that involves using two ANNs, one each for the ON- and OFF-states. A transition layer is also used to model the transition between the ON- and OFF-states. We evaluated our method on training datasets of various sizes. This method achieved a coefficient of determination (R 2) exceeding 99.96% on 3000 I D –V G curves when training was conducted using only 150 randomly selected curves, with a modeling time of less than 10 s. Our approach can thus be used to accurately and efficiently model the I D –V G characteristics of semiconductor devices with large ON/OFF current ratios, such as SiC MOSFETs.

Keywords

• ANN
• Machine learning
• SiC MOSFET
• TCAD
• TCAD-augmented machine learning
• Transfer characteristics