TY - GEN
T1 - ChangeChip
T2 - 2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021
AU - Fridman, Yehonatan
AU - Rusanovsky, Matan
AU - Oren, Gal
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - The usage of electronic devices increases, and becomes predominant in most aspects of life. Surface Mount Technology (SMT) is the most common industrial method for manufacturing electric devices in which electrical components are mounted directly onto the surface of a Printed Circuit Board (PCB). Although the expansion of electronic devices affects our lives in a productive way, failures or defects in the manufacturing procedure of those devices might also be counterproductive and even harmful in some cases. It is therefore desired and sometimes crucial to ensure zero-defect quality in electronic devices and their production. While traditional Image Processing (IP) techniques are not sufficient to produce a complete solution, other promising methods like Deep Learning (DL) might also be challenging for PCB inspection, mainly because such methods require big adequate datasets which are missing, not available or not updated in the rapidly growing field of PCBs. Thus, PCB inspection is conventionally performed manually by human experts. Unsupervised Learning (UL) methods may potentially be suitable for PCB inspection, having learning capabilities on the one hand, while not relying on large datasets on the other. In this paper, we introduce ChangeChip, an automated and integrated change detection system for defect detection in PCBs, from soldering defects to missing or misaligned electronic elements, based on Computer Vision (CV) and UL. We achieve good quality defect detection by applying an unsupervised change detection between images of a golden PCB (reference) and the inspected PCB under various setting. In this work, we also present CD-PCB, a synthesized labeled dataset of 20 pairs of PCB images for evaluation of defect detection algorithms. The sources of ChangeChip, as well as CD-PCB, are available at: https://github.com/Scientific-Computing-Lab-NRCN/ChangeChip.
AB - The usage of electronic devices increases, and becomes predominant in most aspects of life. Surface Mount Technology (SMT) is the most common industrial method for manufacturing electric devices in which electrical components are mounted directly onto the surface of a Printed Circuit Board (PCB). Although the expansion of electronic devices affects our lives in a productive way, failures or defects in the manufacturing procedure of those devices might also be counterproductive and even harmful in some cases. It is therefore desired and sometimes crucial to ensure zero-defect quality in electronic devices and their production. While traditional Image Processing (IP) techniques are not sufficient to produce a complete solution, other promising methods like Deep Learning (DL) might also be challenging for PCB inspection, mainly because such methods require big adequate datasets which are missing, not available or not updated in the rapidly growing field of PCBs. Thus, PCB inspection is conventionally performed manually by human experts. Unsupervised Learning (UL) methods may potentially be suitable for PCB inspection, having learning capabilities on the one hand, while not relying on large datasets on the other. In this paper, we introduce ChangeChip, an automated and integrated change detection system for defect detection in PCBs, from soldering defects to missing or misaligned electronic elements, based on Computer Vision (CV) and UL. We achieve good quality defect detection by applying an unsupervised change detection between images of a golden PCB (reference) and the inspected PCB under various setting. In this work, we also present CD-PCB, a synthesized labeled dataset of 20 pairs of PCB images for evaluation of defect detection algorithms. The sources of ChangeChip, as well as CD-PCB, are available at: https://github.com/Scientific-Computing-Lab-NRCN/ChangeChip.
KW - Change Detection
KW - PCA-Kmeans
KW - PCBs
KW - SMT Quality Control
KW - Unsupervised Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85125817562&partnerID=8YFLogxK
U2 - 10.1109/PAINE54418.2021.9707699
DO - 10.1109/PAINE54418.2021.9707699
M3 - Conference contribution
AN - SCOPUS:85125817562
T3 - Proceedings of the 2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021
BT - Proceedings of the 2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021
PB - Institute of Electrical and Electronics Engineers
Y2 - 30 November 2021 through 2 December 2021
ER -