Deep Learning-Based Defect Detection in Printed Circuit Boards Using Machine Vision

The complexity of Printed Circuit Boards (PCBs) in today's electronics demands accurate and efficient flaw detection. Traditional inspection methods, such as human inspection and Automated Optical Inspection (AOI), often involve precision and flexibility. This paper introduces a novel approach to deep learning-based detection of PCB defects using a Vision Transformer (ViT). The proposed ViT model uses self-attention methods to get both global and local characteristics that help in optimizing the accuracy of fault recognition. This is quite different from traditional CNNs, which cannot capture long-range dependencies efficiently. To employ a hybrid architecture that makes use of the custom anomaly detection head, an efficient patch-wise processing algorithm for images, and convolutional layers with Transformers for better feature extraction. Evaluated on a varied dataset, the model outperforms state-of-the-art CNN-based approaches by achieving 98.7 percent accuracy. The technology proposed here presents a considerable leap in the inspection of PCBs with its high accuracy and real-time processing capabilities, thus offering a practicable and scalable solution for current electronics production.