Wafer-level packaging solder joint reliability lifecycle prediction using SVR-based machine learning algorithm

Abstract

The development of new electronic packaging structures often involves a design-on-simulation (DoS) 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 support vector regression technique, to predict the reliability of the Wafer-Level Package (WLP). By doing so, the study aims to provide designers with a reliable way to assess the reliability life cycle of their packaging designs. The study involves three steps: validating the WLP's reliability using FEA and experiment results, the validated FEA result will serve as input to obtain a predictive model through the SVR technique, and the predictive model's performance will be evaluated. 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.