Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (6), p.3246
• Main Authors: Pham, Thi Tram Anh, Thoi, Do Kieu Trang, Choi, Hyohoon, Park, Suhyun
• Format: Article
• Language: English
• Subjects: Algorithms; Circuit boards; Circuit printing; Deep learning; defect inspection; Defects; Image acquisition; Inspection; Labeling; Machine learning; Model accuracy; noisy training; printed circuit board; Printed circuit boards; Printed circuits; Quality control equipment; semi-supervised learning; Semiconductor industry
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23063246

Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.