Expectation–maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor

Abstract

This paper aims to study the microstructural and micromechanical variations of solder joints in a semiconductor under the evolution of thermal-cycling loading. For this purpose, a model was developed on the basis of expectation-maximization machine learning (ML) and nanoindentation mapping. Using this model, it is possible to predict and interpret the microstructural features of solder joints through the micromechanical variations (i.e. elastic modulus) of interconnection. According to the results, the classification of Sn-based matrix, intermetallic compounds and the grain boundaries with specified elastic-modulus ranges was successfully performed through the ML model. However, it was detected some overestimations in regression process when the interfacial regions got thickened in the microstructure. The ML outcomes also revealed that the thermal-cycling evolution was accompanied with stiffening and growth of intermetallic compounds; while the spatial portion of Sn-based matrix decreased in the microstructure. It was also figured out that the stiffness gradient becomes intensified in the treated samples, which is consistent with this fact that the thermal cycling increases the mechanical mismatch between the matrix and the intermetallic compounds.

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.…”