  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 of the
sensor surface in real time has been developed to automate the visual
inspection. The anomaly detection is done by an ensemble of independent deep
convolutional neural networks: an autoencoder and a classifier. The performance
is evaluated on images acquired in production. The pre-selection reduces the
number of images requiring human inspection by 85%, with recall of 97%. Data
gathered in production can be used for continuous learning to improve the
accuracy incrementally.
