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Article

Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder

1
Department of Biomedical Engineering, Gachon University, 191 Hambakmoe-ro, Incheon 2199, Korea
2
Department of Medial IT Convergence Engineering, Kumoh National Institute of Technology, 350-27 Gum-daero, Gumi 39253, Korea
3
School of Information, University of California, Berkeley, 102 South Hall 4600, Berkeley, CA 94704, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Kim Phuc Tran
Sensors 2021, 21(15), 4968; https://doi.org/10.3390/s21154968
Received: 27 June 2021 / Revised: 18 July 2021 / Accepted: 20 July 2021 / Published: 21 July 2021
As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images. View Full-Text
Keywords: deep learning; autoencoder; detect detection; PCB defeat detection; printed circuit board manufacturing deep learning; autoencoder; detect detection; PCB defeat detection; printed circuit board manufacturing
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MDPI and ACS Style

Kim, J.; Ko, J.; Choi, H.; Kim, H. Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder. Sensors 2021, 21, 4968. https://doi.org/10.3390/s21154968

AMA Style

Kim J, Ko J, Choi H, Kim H. Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder. Sensors. 2021; 21(15):4968. https://doi.org/10.3390/s21154968

Chicago/Turabian Style

Kim, Jungsuk, Jungbeom Ko, Hojong Choi, and Hyunchul Kim. 2021. "Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder" Sensors 21, no. 15: 4968. https://doi.org/10.3390/s21154968

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