Print Email Facebook Twitter Physics-Informed Machine Learning for Solder Joint Qualification Tests Title Physics-Informed Machine Learning for Solder Joint Qualification Tests Author de Jong, S.D.M. (TU Delft Electronic Components, Technology and Materials) Ghorbani Ghezeljehmeidan, A. (TU Delft Electronic Components, Technology and Materials) van Driel, W.D. (TU Delft Electronic Components, Technology and Materials) Date 2024 Abstract The ability to accurately predict the reliability and lifetime of electronics is of great importance to the industry. The failure of the solder joint is of particular interest for these predictions, because of their susceptibility to failure under thermo-mechanical stress. However, the experimental or even conventional simulation techniques employed to estimate the lifetime of a solder joint are often too expensive or time consuming to be of practical use. Therefore, this work introduces a physics-informed Long Short-Term Memory (LSTM) to predict the plastic strain in the critical area of the solder joint. The predicted values are in agreement with the values gained from finite elements, thereby demonstrating the advantage of applying the proposed methodology. Subject Solder Joint ReliabilityPlasticityFinite ElementsPINNLSTM To reference this document use: http://resolver.tudelft.nl/uuid:36ed6906-27f0-4fe4-865a-311a8eb4c414 DOI https://doi.org/10.1109/EuroSimE60745.2024.10491412 Publisher IEEE Embargo date 2024-10-09 ISBN 979-8-3503-9364-4 Source Proceedings of the 2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) Event 2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2024-04-07 → 2024-04-10, Catania, Italy Series 2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2024 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2024 S.D.M. de Jong, A. Ghorbani Ghezeljehmeidan, W.D. van Driel Files file embargo until 2024-10-09