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공과대학
전자전기공학전공
Journal papers
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Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning
Title
Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning
Authors
Pham T.T.A.
;
Thoi D.K.T.
;
Choi H.
;
Park S.
Ewha Authors
박수현
SCOPUS Author ID
박수현
Issue Date
2023
Journal Title
Sensors
ISSN
1424-8220
Citation
Sensors vol. 23, no. 6
Keywords
defect inspection
;
noisy training
;
printed circuit board
;
semi-supervised learning
Publisher
MDPI
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
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. © 2023 by the authors.
DOI
10.3390/s23063246
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