A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny
[Purposes] Catastrophic Optical Damage (COD) is a major limiting factor for the reliability and lifespan of high-power semiconductor lasers, making effective defect detection crucial for optimizing the manufacturing processes and structural designs of laser chips. In this study, a lightweight laser...
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2025-01-01
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| Series: | Taiyuan Ligong Daxue xuebao |
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| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2373.html |
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| author | HU Wei ZHAO Jumin LI Dengao |
| author_facet | HU Wei ZHAO Jumin LI Dengao |
| author_sort | HU Wei |
| collection | DOAJ |
| description | [Purposes] Catastrophic Optical Damage (COD) is a major limiting factor for the reliability and lifespan of high-power semiconductor lasers, making effective defect detection crucial for optimizing the manufacturing processes and structural designs of laser chips. In this study, a lightweight laser chip defect detection algorithm based on an improved YOLOv7-Tiny is proposed, aiming at addressing the high computational and parameter demands of deep learning applications in defect detection. [Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. Additionally, the introduced coordinate attention mechanism improves the precision of defect localization. Pruning experiments and model deployment are conducted to further verify the algorithm practicality. [Findings] Experimental results on the electroluminescence dataset demonstrate that this method can accurately detect chip defects with lower parameter and computational costs, showing excellent performance. |
| format | Article |
| id | doaj-art-72f5846fd5824b7b8388604de2b283e2 |
| institution | Kabale University |
| issn | 1007-9432 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj-art-72f5846fd5824b7b8388604de2b283e22025-02-12T03:34:22ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322025-01-0156113714710.16355/j.tyut.1007-9432.202403881007-9432(2025)01-0137-11A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-TinyHU Wei0ZHAO Jumin1LI Dengao2College of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, ChinaCollege of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China[Purposes] Catastrophic Optical Damage (COD) is a major limiting factor for the reliability and lifespan of high-power semiconductor lasers, making effective defect detection crucial for optimizing the manufacturing processes and structural designs of laser chips. In this study, a lightweight laser chip defect detection algorithm based on an improved YOLOv7-Tiny is proposed, aiming at addressing the high computational and parameter demands of deep learning applications in defect detection. [Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. Additionally, the introduced coordinate attention mechanism improves the precision of defect localization. Pruning experiments and model deployment are conducted to further verify the algorithm practicality. [Findings] Experimental results on the electroluminescence dataset demonstrate that this method can accurately detect chip defects with lower parameter and computational costs, showing excellent performance.https://tyutjournal.tyut.edu.cn/englishpaper/show-2373.htmlcatastrophic optical damagesemiconductor laser chipdefect detectionlightweightmodel pruning |
| spellingShingle | HU Wei ZHAO Jumin LI Dengao A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny Taiyuan Ligong Daxue xuebao catastrophic optical damage semiconductor laser chip defect detection lightweight model pruning |
| title | A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny |
| title_full | A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny |
| title_fullStr | A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny |
| title_full_unstemmed | A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny |
| title_short | A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny |
| title_sort | lightweight laser chip defect detection algorithm based on improved yolov7 tiny |
| topic | catastrophic optical damage semiconductor laser chip defect detection lightweight model pruning |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2373.html |
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