Physics-Informed Machine Learning for Solder Joint Qualification Tests

Physics-Informed Machine Learning for Solder Joint Qualification Tests

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...

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Bibliographic Details
Published in:2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) pp. 1 - 7
Main Authors: De Jong, S.D.M., Ghezeljehmeidan, A.G., Van Driel, W.D.
Format: Conference Proceeding
Language:English
Published: IEEE 07-04-2024
Subjects:
Computational modeling
Finite element analysis
Finite Elements
LSTM
Machine learning
PINN
Plasticity
Plastics
Predictive models
Reliability
Solder Joint Reliability
Soldering
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Summary: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.
ISSN:2833-8596
DOI:10.1109/EuroSimE60745.2024.10491412