| dc.description.abstract | With the rapid advancement of technology driving the leap forward in hardware techniques, there is an escalating demand for circuit boards, paralleled by increasingly stringent quality requirements. Deep learning technology, recognized for its exceptional potential in applications, plays a pivotal role not only in the industrial sector but also in daily life. For instance, in the field of traffic management, deep learning has been implemented to enable intelligent traffic law enforcement, including technological systems at intersections that automatically detect red light violations or speeding, thereby enhancing road safety and enforcement efficiency.
Currently, in the printed circuit board (PCB) manufacturing industry, the inspection of PCB yield primarily relies on Automated Optical Inspection (AOI) systems and manual checking. However, the AOI systems frequently encounter defect judgment errors, leading to substantial human intervention and thus, increasing production costs. To effectively reduce the labor costs associated with PCB inspection, this study proposes a deep learning-based detection technique to identify defects on PCBs. Our goal is to establish a deep learning model that can accurately filter out the ′pseudo defects′ marked by the AOI systems, thereby increasing the precision and efficiency of inspections.
After a series of rigorous tests and evaluations, this research has chosen the YOLO neural network as the principal framework for model training. YOLO, widely applied in academia and industry for its superior object detection capabilities in recent years, is utilized in this study to treat defects as specific objects. Through meticulous training with deep learning, the system is capable of identifying and marking defect locations with high accuracy. The dataset used for model training is comprised of defect data currently collected by the AOI systems on the production line, provided by our industry partners, including eight types of defects and non-defect image data erroneously identified by the AOI systems. | en_US |