ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection

Yehonatan Fridman, Matan Rusanovsky, Gal Oren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665410106
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021 - Virtual, Online, United States
Duration: 30 Nov 20212 Dec 2021

Publication series

NameProceedings of the 2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021

Conference

Conference2021 IEEE International Conference on Physical Assurance and Inspection on Electronics, PAINE 2021
Country/TerritoryUnited States
CityVirtual, Online
Period30/11/212/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

ACKNOWLEDGMENTS We would like to thank Amir Ellenbogen and Gabi Dadush for a fruitful collaboration with our team in this project. We also thank Ori Stern and Ami Moskowitz for helping in the creation of CD-PCB. This work was supported by the Lynn and William Frankel Center for Computer Science. Computational support was provided by the NegevHPC project [27].

FundersFunder number
Lynn and William Frankel Center for Computer Science

    Keywords

    • Change Detection
    • PCA-Kmeans
    • PCBs
    • SMT Quality Control
    • Unsupervised Machine Learning

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