Solder Joint Defect Detection in PCB Chip Components Using Machine Learning: A Review

The functionality and reliability of electronic systems depend heavily on solder joints in printed circuit board (PCB) assemblies. However, the complexity and speed of modern production lines make traditional inspection methods such as rule-based image processing and manual visual checks inadequate. Machine learning (ML) offers a data-driven, automated solution that improves scalability, accuracy, and adaptability in defect detection. This review presents a comprehensive analysis of various ML-based methods, including supervised, unsupervised, ensemble, and hybrid learning approaches for identifying solder joint defects. It highlights key defect types, outlines inspection challenges, and discusses the strengths and limitations of each approach in practical settings.