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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Physics-Informed Machine Learning Framework for Predicting Electromigration Failure in Solder Joints
Author(s) Harikrishnan Kumarasamy, Choong-Un Kim, Sylvester Ankamah-Kusi, Tae-Kyu Lee, Hyuck Mo Lee
On-Site Speaker (Planned) Hyuck Mo Lee
Abstract Scope Electromigration remains a major reliability challenge in advanced electronic packaging, yet current lifetime prediction approaches are often limited to specific materials, geometries, and operating conditions. This work presents the development of a generalized EM failure predictor for solder joints based on a physics-informed machine learning framework. The approach integrates established EM physics, experimental observations, and data-driven modeling to provide accurate, interpretable, and transferable predictions of EM-induced damage and failure. The framework leverages a comprehensive database of experimentally characterized EM failures and a validated Phase-Field simulation model capable of capturing microstructural evolution and damage development under varying current densities, temperatures, solder-joint geometries, and alloy compositions. High-fidelity simulation data are used to train machine learning models that can predict failure behavior in previously untested conditions. The resulting predictive tools are found to provide reasonably accurate quantitative relationships between EM damage kinetics and key physical descriptors. Our methodology and highlighting evidence will be presented.
Proceedings Inclusion? Planned:

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