A Micromechanical Data-Driven Machine-Learning Approach for Microstructural Characterization of Solder Balls in Electronic Packages Subjected to Thermomechanical Fatigue

Abstract

A combination of nanoindentation mapping and machine-earning (ML) modeling has been used to characterize the micro-structural changes in SnPb solder balls exposed to thermal cycling. The model facilitated the microstructural evaluation of solder bumps through the prediction of microscale variations of Young's modulus in the joint zone. The outcomes revealed that the micromechanical data-driven ML model precisely classified the microstructural constituents, i.e., β-Sn and α-Pb, along with the grain boundary (GB) regions. However, some deviations were observed in GB recognition, when the elastic modulus gradient was not sharp enough. The predictive results also revealed that the increase in number of thermal cycles led to stiffening and grain coarsening of α-Pb, while the β-Sn matrix mainly remained stable. Moreover, it was found that the thermal cycling intensified structural heterogeneity in the solder and sharpened the elastic modulus variations at the GB regions. In summary, the outcomes of this study demonstrate the prediction possibility of microstructural features in SnPb solder balls with a predefined thermal cycle numbers, and unfolded the relationship between morphological characteristics and microscale mechanical properties.

Low-cycle fatigue life assessment of SAC solder alloy through a FEM-data driven machine learning approach

“…Over the past few years, machine learning (ML) methods and statistical approaches have been increasingly used to evaluate the fatigue characteristics of SAC solder joints (Samavatian et al, 2020b;Kurniawan et al, 2023;Chen, 2023;Chen et al, 2022bChen et al, , 2022aXiong et al, 2020). For instance, Samavatian et al (2022b) proposed an iterative ML framework that improved the accuracy of predicting the useful lifetime of solder joints in electronic devices.…”