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This review aims to analyze how machine learning methodologies are improving solder joint engineering and reliability predictions.

Machine learning in solder joint engineering: a review of microstructure informatics, reliability prediction, and accelerated design

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Review examines how machine learning enhances reliability predictions in solder joints, indicating significant improvements.

This review aims to analyze how machine learning methodologies are improving solder joint engineering and reliability predictions.
Synthesized advances in machine learning applications for solder joint engineering across three key areas.
Evaluated 18 studies focusing on microstructure informatics, predictive reliability modeling, and inverse design.
Addressed challenges like data limitations and model interpretability.
Convolutional neural networks achieved 95% accuracy in microstructure segmentation.
Multi-objective Bayesian optimization identified novel SAC-based alloy with over 20% improvement in strength-ductility.
Long short-term memory networks reduced simulation times from hours to seconds with less than 5% error.

Muhammad Nubli Zulkifli (2026) studied this question.

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