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In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modulesare fabricated in advanced laboratories around the world. Each sensor modulecontains about 700 checkpoints for visual inspection thus making it almostimpossible to carry out such inspection manually. As artificial intelligence ismore and more widely used in manufacturing, traditional detection technologiesare gradually being intelligent. In order to more accurately evaluate thecheckpoints, we propose to use deep learning-based object detection techniquesto detect manufacturing defects in testing large numbers of modulesautomatically.

Automated visual inspection of silicon detectors in CMS experiment