A Comparative Analysis of Printed Circuit Boards Defect Detection Leveraging Deep Learning Approaches

Printed circuit boards (PCBs) are the foundations of modern industry. Their prevalence across electronic industries is explicit, ranging from computers and smartphones to televisions, digital cameras, and other devices. However, during the production process of PCBs, defects are inevitable, leading to substandard products, hence the detection of defects is a crucial task. Deep learning offers significant advantages over conventional machine learning approaches. These advantages include but not limited to the capacity to automatically extract complex embeddings from input samples, leading to improved accuracy and performance. Fueled deep learning, algorithms designed for PCB defect detection are anticipated to deliver superior precision and efficiency, attributed to their capability to recognize novel defect patterns. This article provides a comprehensive analysis of these algorithms, separated into three major categories, including Transformer-based solutions, one-stage, and two-stage approaches. Improvement methods and advantages of these algorithms are detailed. Finally, prospective future research is pinpointed to tackle existing challenges within the algorithms.