 |
Printed Circuit Boards, or PCBs, are fundamental components in modern electronic devices. They serve as the primary platform for electrical connectivity and signal transmission. With the rapid growth of telecommunications, automotive electronics, and consumer devices, PCB designs have become increasingly complex, featuring higher component density and miniaturization.
Ensuring defect-free PCBs is therefore critical. Even minor defects such as missing tracks, short circuits, or surface contamination can significantly compromise product reliability, safety, and cost efficiency.
Conventional inspection methods, including manual visual inspection and rule-based machine vision systems, have limited scalability, subjectivity, and low adaptability to complex defect patterns. While supervised learning approaches can improve accuracy, they require extensive labeled datasets that are costly and time-consuming to collect. In practice, it is nearly impossible to capture every possible defect variation that may occur on real manufacturing lines.
To overcome these limitations, we propose an unsupervised learning-based defect detection framework. This approach leverages convolutional autoencoders to learn standard pattern representations from defect-free PCB images, enabling anomaly detection through reconstruction error analysis without requiring labeled defect data. |
IEEE/ICACT20260190 Slide.04
[Big Slide]