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Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
More than a thousand 8″ silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1088/2632-2153/aced7e http://cds.cern.ch/record/2856526 |