AI-Based PCB Micro-Defect Detection: A Conceptual Framework and Case-Based Validation

Printed Circuit Boards (PCBs) are necessary parts of current electronic products, and surface defects can cause problems in the operation or lifespan of these devices. Traditional inspection methods are unable to find small defects, are easily affected by a cluttered background, and have low inspection efficiency. Therefore, deep learning and object detection technologies have been applied to automate the inspection of PCB defects. This paper investigates how artificial intelligence (AI) can be used to address the problems of detecting small defects on PCBs and builds a conceptual system that integrates the attributes of PCB defects with AI inspection technology. Based on the review of previous studies, this paper presents an all-encompassing system architecture for defect attributes, AI feature learning, defect detection and localisation, and performance evaluation; four representative case studies are used to verify the system's practicality. Based on the above results, using small-object detection, attention mechanisms, improved feature extraction methods and lightweight network architectures can improve both the detection accuracy and efficiency. In addition, all the above factors should be taken into account in the process of defect detection. A limitation of this study is that it is based on existing literature and case analyses without new experimental verification; in the future, further tests of the framework can be conducted using publicly available PCB defect datasets.