AI-assisted reliability life prediction model for wafer-level packaging using the random forest method

Abstract

ABSTRACT We applied an artificial intelligence (AI) algorithm, the random forest (RF) model, using finite element simulations to predict the reliability life of wafer-level packaging (WLP). Due to rapid growth and an increasingly fast cycle pace of integrated circuits, it is imperative to shorten the development time of electronic packaging. This study focuses on packaging reliability analysis and prediction. In recent years, package reliability analysis has been performed using finite element method simulations, which reduce the required number of accelerated thermal cyclic tests. Compared with conventional ball grid array-type packaging, WLP has become the mainstream packaging type due to its small form factor, batch-type manufacturing process and low cost. We applied the RF model, a machine learning algorithm, to predict the reliability life of WLP. The finite element procedure, theory and mesh size were validated by a set of experiments, and a large dataset was generated for AI training purposes through the finite element simulations. The RF method was built using Python®. A fast and robust reliability assessment AI model of WLP can be achieved once the AI training accuracy is located within the target range; the designer only needs to input the geometry of each WLP component to obtain the reliability life cycle. WLP structural optimization can thus be easily achieved. The AI model also significantly shortens the design cycle to meet current design demands.