@inproceedings{4b5d1b17b5b84cfdaa36744d7c75b6d8,
title = "DC Power Cycling Test and Lifetime Prediction for SiC MOSFETs",
abstract = "The degradation and failure during long-term operation restricts the wide application of silicon carbide metal-oxide-semiconductor field effect transistors (SiC MOSFETs). And accelerated lifetime test (ALT) and lifetime prediction methods are commonly used to estimate and improve the reliability of power devices. However, most of existing ALTs are designed for silicon insulated gate bipolar transistors (Si IGBTs) and the existing lifetime prediction methods have a low accuracy. A DC power cycling topology for SiC MOSFETs is designed in this paper, which can accelerate the aging of multiple devices simultaneously, and the test platform is set up. The on-state drain-source voltage (Vds-on) is selected as an aging precursor and measured on line. Besides, A long short-term memory (LSTM)-based lifetime prediction method is proposed. The change of Vds-on with the aging of SiC MOSFSTs is predicted. And experimental results show that the proposed method has a high prediction accuracy.",
keywords = "DC Power Cycling test, Lifetime Prediction, Reliability, SiC MOSFETs",
author = "Xiaofeng Ding and Binbin Wang and Yanyong Yang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 26th International Conference on Electrical Machines and Systems, ICEMS 2023 ; Conference date: 05-11-2023 Through 08-11-2023",
year = "2023",
doi = "10.1109/ICEMS59686.2023.10344327",
language = "英语",
series = "2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4638--4643",
booktitle = "2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023",
address = "美国",
}