Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules-Reference-Cited by-同舟云学术

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Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

Published:2024-04-07 Issue: Volume: Page:1-8
ISSN:
Container-title:2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
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Author:

Stoyanov Stoyan¹,Tilford Tim¹,Zhang Xiaotian²,Hu Yihua³,Yang Xingyu⁴,Shen Yaochun⁴

Affiliation:

1. University of Greenwich,School of Computing and Mathematical Sciences,London,UK

2. University of York,Department of Electronic Engineering,York,UK

3. King’s College London,Engineering, Faculty of Natural, Mathematical & Engineering Sciences,London,UK

4. University of Liverpool,Department of Electrical Engineering and Electronics,Liverpool,UK

Funder

Engineering and Physical Sciences Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10491281/10491408/10491522.pdf?arnumber=10491522

Reference15 articles.

1. Power Electronics Reliability: State of the Art and Outlook

2. Automotive Power Module Packaging: Current Status and Future Trends

3. Enabling high reliability power modules: A multidisciplinary task

4. Review of the Failure Mechanism and Methodologies of IGBT Bonding Wire

5. A Finite Element Analysis on the Reliability of Heavy Bonding Wire for High-Power IGBT Module

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