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

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 (R²) 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.