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...
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| creator | Giri, Nupur Dugad, Shashi Chhabria, Amit Manwani, Rashmi Asrani, Priyanka |
| 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. |
| doi_str_mv | 10.48550/arxiv.2206.02572 |
| format | Article |
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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
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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
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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.</abstract><doi>10.48550/arxiv.2206.02572</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Physics - Instrumentation and Detectors |
| title | Automated visual inspection of silicon detectors in CMS experiment |
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