Application of machine learning modeling for predicting the reliability of solder joints under thermal cycling
Qiulin Yu (Polymer Competence Center Leoben GmbH, Austria Technologie & Systemtechnik Aktiengesellschaft, Montanuniversität Leoben )
Chinmay Nawghane (IMEC-Solliance)
Zihan Zhang (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Bart Vandevelde (IMEC-Solliance)
Karl Fendt (Austria Technologie & Systemtechnik Aktiengesellschaft)
Thomas Krivec (Austria Technologie & Systemtechnik Aktiengesellschaft)
Dieter P. Gruber (Polymer Competence Center Leoben GmbH, Montanuniversität Leoben )
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Abstract
In this study, Machine Learning (ML) methods combined with Optuna hyperparameter optimization were investigated to predict creep strain in solder joints of multilayer chip capacitors. Material properties, geometry and thermal loading conditions were varied in simulations using Finite Element Modeling. Evaluated ML models included Random Forest, Gradient Boosting, Support Vector Regression (SVR) and Artificial Neural Network (ANN). The results demonstrated a prediction accuracy of 96%, particularly for SVR and ANN. The model performance significantly improved with increasing data size up to around 600 simulations. In the feature and hyperparameter importance analysis, solder stand-off height and component length most influenced ANN predictions, with learning rate being the key hyperparameter, while for SVR, the regularization parameter or kernel function was most critical.
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