  The growing complexity of particle detectors makes their construction and
quality control a new challenge. We present studies that explore the use of
deep learning-based computer vision techniques to perform quality checks of
detector components and assembly steps, which will automate procedures and
minimize the need for human interventions. This study focuses on the
construction steps of a silicon detector, which involve forming a mechanical
structure with the sensor and wire bonding individual cells to electronics for
reading out signals. Silicon detectors in high energy physics experiments today
have millions of channels. Manual quality control of these and other high
channel-density detectors requires enormous amounts of labor and can be prone
to errors. Here, we explore computer vision applications to either augment or
fully replace visual inspections done by humans. We investigated convolutional
neural networks for image classification and autoencoders for anomalies
detection. Two proof-of-concept studies will be presented.
