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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...

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Detalles Bibliográficos
Autores principales: Grönroos, Sonja, Pierini, Maurizio, Chernyavskaya, Nadezda
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:https://dx.doi.org/10.1088/2632-2153/aced7e
http://cds.cern.ch/record/2856526