@inproceedings{11e63490dda440f9a5140a7ca998154b,
title = "Neural Network-based Classification of Breakdown Mechanisms and Prediction of breakdown Voltage and On-resistance for 4H-SiC Trench Gate MOS Devices",
abstract = "Since neural networks do not face the convergence issues like TCAD simulations, we attempted to construct two types of neural networks for numerical prediction and mechanism classification, respectively. After training with the SiC trench gate MOS dataset obtained from different structural parameters using TCAD simulations, the accurate prediction results were achieved. During the validation process, the breakdown type classifier achieves an accuracy of 96.5%, and the numerical predictors for breakdown voltage and on-resistance have the average errors within 10%.",
keywords = "Neural Network, Power Device, Silicon Carbide, TCAD simulations",
author = "Jiaxi Zhang and Shiyan Zhang and Ze Sun and Yucheng Wang and Yupan Wu and Wei Li and Shaoxi Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Symposium of Electronics Design Automation, ISEDA 2024 ; Conference date: 10-05-2024 Through 13-05-2024",
year = "2024",
doi = "10.1109/ISEDA62518.2024.10617680",
language = "英语",
series = "2024 International Symposium of Electronics Design Automation, ISEDA 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "780--781",
booktitle = "2024 International Symposium of Electronics Design Automation, ISEDA 2024",
}