Automated visual inspection and defect detection of large-scale silicon strip sensors. (1st March 2022) - Affolder, K.; Ciocio, A.; Cornell, E.; Fadeyev, V.; Luce, Z.; Gunnell, J.; Martinez-McKinney, F.; Johnson, T.; MacFadyen, R.; Poley, L.; Wilson, K.
Abstract: For the Phase-II Upgrade of the ATLAS Detector, the Inner Detector will be replaced with the Inner Tracker (ITk), consisting of a pixel and a strip tracker. The 17, 888 silicon strip detector modules comprising the ITk strip tracker will be assembled from silicon strip sensors and flexes with readout chips in a manual assembly process performed at 20 module assembly sites in a complex distribution chain, which requires quality control steps to be performed after each distribution and assembly step. Sensor quality control requires a visual inspection of the full sensor area (about 100 cm 2 ) of each sensor to detect and log any defects (e.g. scratches, breakdown areas or chipped corners) or contamination. Since manual surveys of full sensor areas for several thousand sensors are both time-consuming and prone to errors, alternative methods were investigated to automate the process and improve its reliability. This paper presents a setup developed to take high-resolution images of full silicon strip sensors with high repeatability quickly and an algorithm developed for the automated detection of defects, built using functions and filters from popular open-source visual processing packages OpenCV and Scikit-image. Methods were developed both for small-scale high-resolution images and full-size sensor images with lower resolution — both are presented here.