Wafer-level packaging solder joint reliability lifecycle prediction using SVR-based machine learning algorithm
Hsuan-Chen Kuo, Chih-Yi Chang, Cadmus Yuan, Kuo‐Ning Chiang
Abstract
Abstract The development of new electronic packaging structures often involves a design-on-simulation approach. However, simulation results can be subjective, and there can be variances in outcomes depending on who is conducting the simulation. To address this issue, packaging designers are now turning to machine learning to increase the accuracy and efficiency of the design process. This research study focuses on using support vector regression (SVR) techniques, such as single kernel, multiple kernels and a new SVR technique, to predict the reliability of the wafer-level packaging (WLP). By doing so, the study aims to provide designers with a reliable way to assess the reliability life cycle of their packaging designs. This research includes three steps: validating the WLP's reliability using finite element analysis (FEA) and experiment results, using the validated FEA result as input to obtain a predictive model through the SVR technique and the evaluating predictive model's performance. The results show that the predictive models developed using the SVR technique have stable performance on different testing data, which is consistent with the FEA results.