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

Sensors

Jungsuk Kim¹  Jungbeom Ko¹  Hojong Choi²  Hyunchul Kim³ 

[1] Department of Biomedical Engineering, Gachon University, 191 Hambakmoe-ro, Incheon 2199, Korea;Department of Medial IT Convergence Engineering, Kumoh National Institute of Technology, 350-27 Gum-daero, Gumi 39253, Korea;School of Information, University of California, Berkeley, 102 South Hall 4600, Berkeley, CA 94704, USA;

关键词: deep learning;    autoencoder;    detect detection;    PCB defeat detection;    printed circuit board manufacturing;   

DOI  :  10.3390/s21154968

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown