Benchmarking Machine Learning Methods Against Finite Element and Empirical Models for Predicting Solder Joint Characteristic Life

Accurate prediction of solder joint characteristic life is crucial for ensuring the reliability of electronic assemblies, especially under severe environmental conditions. This study benchmarks the predictive capabilities of machine learning (ML) methods against traditional empirical power-law models and finite element analysis (FEA) performed using ANSYS, employing a comprehensive dataset comprising 185 experimental test results. The ML approach consolidates diverse isothermal aging and thermal cycling conditions into a unified predictive framework, contrasting sharply with empirical and finite element method (FEM) models, which require condition-specific recalibration and extensive simulation resources. FEM simulations conducted in ANSYS provided valuable physical insights but were limited by intensive computational demands and modeling complexity, while empirical models showed decreasing accuracy at higher temperatures and complex component configurations. ML algorithms, notably XGBoost and artificial neural networks (ANN), achieved superior accuracy, robustness, and scalability, effectively capturing complex nonlinear interactions between continuous (e.g., aging duration, temperature) and categorical variables (e.g., surface finish, solder alloy). Quantitative analysis confirmed ML’s consistent prediction accuracy (errors typically less than 3%) across varied test scenarios, significantly surpassing empirical and FEM approaches. These findings underscore the transformative potential of ML methods for enhancing solder joint reliability predictions by significantly reducing computational overhead, modeling complexity, and prediction errors.