Automated visual inspection system for ATLAS ITk strip hybrids

A method of automating the visual inspection of ATLAS upgrade strip modules is shown. The visual inspection of the hybrids is a time consuming part of the quality control during module production. A method of detecting and classifying the SMD components on the hybrids using an object detection neural network was investigated. The results show that the amount of hybrids that needed to be check by a human operator was reduced to around 10% of the batch. This hugely reduced the amount of time needed for human inspection and did find real mistakes done during the production of the hybrids.

Automated summary

A method of automating the visual inspection of ATLAS upgrade strip modules was introduced, which involved using an object detection neural network to detect and classify SMD components on the hybrids. The results demonstrated a significant reduction in the amount of human inspection required, with only around 10% of the batch needing manual checking. This method also successfully identified real mistakes made during hybrid production.