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 learningbased 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. Keywords: Anomaly detection, autoencoder, convolutional deep neural networks, silicon sensors, quality control, visual inspection 1. Introduction Silicon sensors are used in high-energy physics experiments due to their sufficient radiation tolerance, energy resolution and cost-effectiveness. In the high radiation area, the active element of the High-Granularity Calorimeter (HGCAL) [1], which will replace the endcap calorimeters of the CMS [2] experiment at the Large Hadron Collider (LHC) [3], will consist of more than 27,000 hexagonal 8" silicon sensor wafers to achieve unprecedented transverse and longitudinal segmentation.