The increasing miniaturization and performance demands in electronics systems have elevated reliability requirements for solder interconnects, which serve as critical electrical, thermal and mechanical interfaces. This review aims to systematically examine how machine learning (ML) methodologies are transforming solder joint engineering by enabling accelerated materials discovery, predictive reliability assessment and microstructure-informed design.
This paper provides a comprehensive synthesis of recent advances in ML applications to solder joint engineering, structured around three interconnected thrusts: (1) microstructure informatics encompassing automated segmentation and generative modelling, (2) predictive reliability modelling including physic-informed neural networks and surrogate models, and (3) inverse design frameworks for alloy optimization and geometric parameterization. A comprehensive analysis of 18 state-of-the-art studies evaluates methodological approaches, data sources, performance metrics and limitations.
ML techniques demonstrate transformative potential across the solder engineering pipeline. Convolution neural networks achieve 95% accuracy in microstructure segmentation, while generative adversarial networks and variational autoencoders enable property-to-microstructure inverse design despite data scarcity. Long short-term memory networks reduce crystal plasticity simulation times from hours to second with < 5% error. Physics-informed neural networks ensure physical consistency in plastic strain predictions. Multi-objective Bayesian optimization frameworks have successfully identified novel SAC-based alloy with > 20% improvement in strength-ductility combinations validated experimentally.
Persistent challenges include limited curated data sets, model interpretability, uncertainty quantification and physical plausibility. Emerging migration strategies like transfer learning, synthetic data generation, explainable AI and physics-informed architecture are critically assessed.
The integration of ML into solder reliability workflows offers potential for reducing accelerated testing cycles, optimizing process parameters and accelerating qualification of new alloys for high-reliability applications.
This review uniquely synthesizes advances across microstructure informatics, reliability prediction and inverse design within a unified Processing-Structure-Property-Performance framework, providing a forward-looking roadmap for intelligent, data-centric electronic packaging design.
