Demonstration of accurate ID-VG characteristics modeling in SiC mosfets using separated artificial neural networks with small training dataset.
Journal:
Scientific reports
Published Date:
May 29, 2025
Abstract
This study developed a novel approach based on separated artificial neural networks (ANNs) to efficiently and accurately model the drain current (I)-gate voltage (V) characteristics of silicon carbide (SiC) power MOSFETs efficiently and accurately. We found that a single ANN cannot model the entire I-V range under a large ON/OFF current ratio (10 to 10 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) exceeding 99.96% on 3000 I-V 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-V characteristics of semiconductor devices with large ON/OFF current ratios, such as SiC MOSFETs.
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