An Adaptive Machine Learning Method Based on Finite Element Analysis for Ultra Low-k Chip Package Design

• Published in: IEEE transactions on components, packaging, and manufacturing technology (2011) Vol. 11; no. 9; pp. 1435 - 1441
• Main Authors: Chu, Weishen, Ho, Paul S., Li, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive sampling; Algorithms; chip packaging reliability; Data models; Design optimization; Design parameters; Finite element analysis; Finite element method; Machine learning; machine learning (ML); Mathematical models; Package design; Packaging; Packaging design; Reliability analysis; Reliability engineering; Sampling methods; Sequential sampling; steepest descent algorithm; Training; ultra low-k
• ISSN: 2156-3950; 2156-3985
• DOI: 10.1109/TCPMT.2021.3102891

Machine learning (ML) is widely used for building data-driven models that are highly useful for optimization. In this study, a finite element model-based adaptive ML method is presented for chip package reliability prediction and design optimization. This ML method employs a validated multi-scale finite element model for training data generation. An adaptive sampling scheme is developed to optimize the training process with a steepest descent algorithm. The developed method was used to optimize ultra low-k chip package design. The effects of ten key design parameters on chip packaging reliability were considered. Multiple ML algorithms were evaluated for model development. It is shown that the adaptive sampling method performs much better than existing sequential sampling methods and that the finite element-based ML model can be used to achieve improved prediction accuracy for chip package design optimization.