The feature extraction and analysis of flaw detection and classification in BGA gold-plating areas

In this paper, the measurements along with color image segmentation to detect all possible defects in BGA (ball grid array) type PCB (printed circuit boards) were presented. We use feature extraction and analysis as well as BPN (back-propagation neural) network classification to classify the detected defects. There are variable defects to be detected and classified including stain, scratch, solder-mask, and pinhole. The experimental results show that the proposed algorithm is successful in detecting and classifying the defects on gold-plating regions. The recognition speed becomes faster and the system becomes more flexible in comparison to the previous system. The proposed method, using unsophisticated and economical equipment, is also verified in providing highly accurate results with a low error rate.