@inproceedings{0c4ee98b992742298bf8ed3ea349553e,
title = "Application of artificial intelligence for modeling SiC Power MOSFETs",
abstract = "An accurate and flexible neural network(NN)-based I-V model for SiC MOSFET is created in this paper. The technique starts with the generating TCAD simulation of close to 200 SiC MOSFETs with variations in key physical parameters. Compact model formulations and their parameters are automatically generated from the variations in the TCAD simulations using machine learning techniques. The NN-based compact model is then trained using 78k data points using the TCAD simulated data. The unphysical behavior of NN-based compact models having a negative gm and gD, causing convergence issues during circuit simulation is also tackled for the first time. The fully trained NN-based compact model results show good accuracy with the fitting I-V, gm and gD for multiple devices. The developed NN-based is then converted to a standard Verilog-A file which can be imported into a circuit simulator.",
keywords = "SiC MOSFET, machine learning, neural network (NN), parameter generation",
author = "Fredo Chavez and Danial Bavi and Sourabh Khandelwal",
year = "2025",
doi = "10.1109/APEC48143.2025.10977386",
language = "English",
isbn = "9798331516123",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "385--388",
booktitle = "2025 IEEE Applied Power Electronics Conference and Exposition (APEC)",
address = "United States",
note = "14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025 ; Conference date: 16-03-2025 Through 20-03-2025",
}