Micro Defect Detection of Printed Circuit Board Using Neural Network Concepts

The integrity of Printed Circuit Boards (PCBs) is fundamental to the reliability of modern electronics. As component density increases, micro defects flaws often smaller than 100μm, such as trace shorts, pinholes, or misaligned viaspose a significant challenge, leading to device failure and reduced manufacturing yield. This paper presents a novel, automated inspection system leveraging a **Convolutional Neural Network (CNN)** for the precise, high-resolution detection and classification of these critical micro defects. Our approach integrates industrial **high- magnification imaging** with a robust, segmentation- based CNN architecture, which is rigorously trained to perform pixel-level fault localization. The system is designed for seamless deployment into high-volume production lines, providing significantly enhanced inspection speed and accuracy compared to traditional methods. Experimental validation demonstrates that the CNN model achieves high recall and precision in defect categorization, drastically reducing manual dependence and minimizing false alarms. Furthermore, the system provides immediate, actionable feedback to the fabrication process, enabling proactive quality control and securing high manufacturing efficiency for complex, miniaturized PCBs.