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

1000多个8英寸硅传感器将进行目测检查，以查看 在装配前的质量控制过程中出现表面异常 进入用于欧洲核子研究中心CMS实验的高粒度量热计。一片深渊 基于学习的算法，预先选择潜在的异常图像 开发了实时传感器表面以实现视觉的自动化 检查。异常检测由独立深度集合来完成 卷积神经网络