Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

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....

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Bibliographic Details
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
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23063246