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Automated visual inspection of silicon detectors in CMS experiment
In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost impossible to carry out such inspection manually. As artificial intell...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2812137 |
| _version_ | 1780973323771969536 |
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| author | Giri, Nupur Dugad, Shashi Chhabria, Amit Manwani, Rashmi Asrani, Priyanka |
| author_facet | Giri, Nupur Dugad, Shashi Chhabria, Amit Manwani, Rashmi Asrani, Priyanka |
| author_sort | Giri, Nupur |
| collection | CERN |
| description | In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost impossible to carry out such inspection manually. As artificial intelligence is more and more widely used in manufacturing, traditional detection technologies are gradually being intelligent. In order to more accurately evaluate the checkpoints, we propose to use deep learning-based object detection techniques to detect manufacturing defects in testing large numbers of modules automatically. |
| id | cern-2812137 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2022 |
| record_format | invenio |
| spelling | cern-28121372023-06-28T03:48:20Zhttp://cds.cern.ch/record/2812137engGiri, NupurDugad, ShashiChhabria, AmitManwani, RashmiAsrani, PriyankaAutomated visual inspection of silicon detectors in CMS experimentcs.LGComputing and Computersphysics.ins-detDetectors and Experimental TechniquesIn the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost impossible to carry out such inspection manually. As artificial intelligence is more and more widely used in manufacturing, traditional detection technologies are gradually being intelligent. In order to more accurately evaluate the checkpoints, we propose to use deep learning-based object detection techniques to detect manufacturing defects in testing large numbers of modules automatically.arXiv:2206.02572oai:cds.cern.ch:28121372022-06-03 |
| spellingShingle | cs.LG Computing and Computers physics.ins-det Detectors and Experimental Techniques Giri, Nupur Dugad, Shashi Chhabria, Amit Manwani, Rashmi Asrani, Priyanka Automated visual inspection of silicon detectors in CMS experiment |
| title | Automated visual inspection of silicon detectors in CMS experiment |
| title_full | Automated visual inspection of silicon detectors in CMS experiment |
| title_fullStr | Automated visual inspection of silicon detectors in CMS experiment |
| title_full_unstemmed | Automated visual inspection of silicon detectors in CMS experiment |
| title_short | Automated visual inspection of silicon detectors in CMS experiment |
| title_sort | automated visual inspection of silicon detectors in cms experiment |
| topic | cs.LG Computing and Computers physics.ins-det Detectors and Experimental Techniques |
| url | http://cds.cern.ch/record/2812137 |
| work_keys_str_mv | AT girinupur automatedvisualinspectionofsilicondetectorsincmsexperiment AT dugadshashi automatedvisualinspectionofsilicondetectorsincmsexperiment AT chhabriaamit automatedvisualinspectionofsilicondetectorsincmsexperiment AT manwanirashmi automatedvisualinspectionofsilicondetectorsincmsexperiment AT asranipriyanka automatedvisualinspectionofsilicondetectorsincmsexperiment |