Interpretable data-driven framework for life prediction of homogenous lead-free solder joints in ball grid array packages

Abstract Designing reliable electronic devices depends heavily on the reliability of solder joints. However, due to the ongoing advancement of novel lead-free solder materials, assessing their reliability for various applications, particularly in extreme conditions, often involves costly and time-consuming tests. Multiple factors, including board, package, assembly parameters, solder material properties, and process parameters, all affect the reliability of solder joints. Leveraging the available reliability test data as well as the simulated data, an interpretable data-driven framework is proposed by integrating linear mixed-effects (LME) models and artificial neural network (ANN) with sensitivity analysis to achieve accurate life predictions of solder materials and improve interpretability for understanding new lead-free materials. Multiple linear regression (MLR) and decision tree (DT) models have also been included for comparison. For thermal cycling (TC) tests, ANN, LME, and MLR achieve high prediction accuracy (R-squared values of 88.95%, 91.79%, and 90.07%, respectively), which are approximately 10%, 14%, and 13% higher than DT. Moreover, the interpretation derived from the random effects of LME and the sensitivity analysis of ANN, along with the insights from MLR and DT, successfully extract valuable information about the life of solder materials, which aligns with experimental testing results and empirical physics knowledge. For instance, it is found that the impact of aging duration on reliability depends on the configuration of the tested electronic package. Furthermore, as the temperature range of the TC test increases, the significance of the solder alloy material’s influence on reliability decreases. This reduction in relevance is associated with a transition to the failure mode under more extreme temperature conditions. Therefore, the interpretable data-driven framework not only models the complex relationships among influencing factors and solder joint reliability but also enables understanding the solder joint reliability. This framework can contribute to accelerating the design and adoption of new lead-free solder materials in various electronic applications and utilizing data-driven models to assess the reliability of electronic packages in future research.