Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

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.

Comparison of Object Region Segmentation Algorithms of PCB Defect Detection

“…The two basic operations of morphology are corrosion and expansion, which are the basis of many morphological treatments. The mathematical expressions of corrosion and expansion operations are shown in Formulas (7) and (8).…”

“…In the study [7], a series of image processing algorithms were used for threshold segmentation and feature extraction of solder joint region in the image, which realized defect detection. As for the low-efficiency traditional sorting of PCB defects in the semiconductor industry, a supervised learning-based model was applied to sort PCB defect detection by Pham et al [8]. The K-means clustering segmentation algorithm was used by Niu et al [9] to detect the bare PCB, which aimed to improve the detection accuracy and speed.…”

A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing Systems

“…The specific implementation of the data expansion (DE) strategy is to use labeled samples of other datasets to extend the target dataset to improve the detection accuracy [5]. When training with labeled and unlabeled data, perturbing the unsupervised loss of unlabeled data with two different augmentations helps improve the performance of the model in the case of insufficient or incorrect data labeling [6]. With the continuous development of deep learning combined with the advantages of conditional GAN, Trans GAN, and YOLOV5, the trained model can generate high-quality synthetic images conditional on class embedding, enhance the number and diversity of the original training set, further improve the accuracy of PCB electronic component identification, and detect, classify, and locate multiple defects in low-resolution bare board PCB images [7,8].…”