Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey
Printed circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10044670/ |
| _version_ | 1797900241166401536 |
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| author | Qin Ling Nor Ashidi Mat Isa |
| author_facet | Qin Ling Nor Ashidi Mat Isa |
| author_sort | Qin Ling |
| collection | DOAJ |
| description | Printed circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs needs to be achieved. This paper reviews various defect detection methods in PCBs by analysing more than 100 related articles from 1990 to 2022. The methodology of how to prepare this overview of the PCB defect detection methods is firstly introduced. Secondly, manual defect detection methods are reviewed briefly. Then, traditional image processing-based, machine learning-based and deep learning-based defect detection methods are discussed in detail. Their algorithms, procedures, performances, advantages and limitations are explained and compared. The additional reviews of this paper are believed to provide more insightful viewpoints, which would help researchers understand current research trends and perform future work related to defect detection. |
| first_indexed | 2024-04-10T08:42:52Z |
| format | Article |
| id | doaj.art-b42c80afd41f421caaa79e680c300813 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T08:42:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b42c80afd41f421caaa79e680c3008132023-02-23T00:00:49ZengIEEEIEEE Access2169-35362023-01-0111159211594410.1109/ACCESS.2023.324509310044670Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A SurveyQin Ling0https://orcid.org/0000-0003-0957-4804Nor Ashidi Mat Isa1https://orcid.org/0000-0002-2675-4914School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaSchool of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang, MalaysiaPrinted circuit boards (PCBs) are a nearly ubiquitous component of every kind of electronic device. With the rapid development of integrated circuit and semiconductor technology, the size of a PCB can shrink down to a very tiny dimension. Therefore, high-precision and rapid defect detection in PCBs needs to be achieved. This paper reviews various defect detection methods in PCBs by analysing more than 100 related articles from 1990 to 2022. The methodology of how to prepare this overview of the PCB defect detection methods is firstly introduced. Secondly, manual defect detection methods are reviewed briefly. Then, traditional image processing-based, machine learning-based and deep learning-based defect detection methods are discussed in detail. Their algorithms, procedures, performances, advantages and limitations are explained and compared. The additional reviews of this paper are believed to provide more insightful viewpoints, which would help researchers understand current research trends and perform future work related to defect detection.https://ieeexplore.ieee.org/document/10044670/Defect detectionPCBimage processingmachine learningdeep learning |
| spellingShingle | Qin Ling Nor Ashidi Mat Isa Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey IEEE Access Defect detection PCB image processing machine learning deep learning |
| title | Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey |
| title_full | Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey |
| title_fullStr | Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey |
| title_full_unstemmed | Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey |
| title_short | Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey |
| title_sort | printed circuit board defect detection methods based on image processing machine learning and deep learning a survey |
| topic | Defect detection PCB image processing machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10044670/ |
| work_keys_str_mv | AT qinling printedcircuitboarddefectdetectionmethodsbasedonimageprocessingmachinelearninganddeeplearningasurvey AT norashidimatisa printedcircuitboarddefectdetectionmethodsbasedonimageprocessingmachinelearninganddeeplearningasurvey |