Machine learning-driven design and optimization of electronic packaging: applications and future developments
Xiangyu Chen, Song He, Kyung-Wook Paik, Yew Hoong Wong, Shuye Zhang
Abstract
Machine learning (ML) provides robust solutions for electronic packaging, where growing complexity and miniaturization challenge traditional methods in design, defect detection, and performance optimization. This review systematically covers ML applications across key areas in electronic packaging, such as defect detection, material optimization, and reliability analysis, discussing key algorithms, data workflows, inherent challenges, and prospects. It aims to provide a clear roadmap and reference for effectively applying ML to innovate in this rapidly evolving field. However, addressing persistent challenges in data quality, model adaptability, and integration with established engineering practices remains vital for continued progress in this domain.