Automated analysis of the visual properties of superconducting detectors

TL;DR

This paper presents an automated computer vision-based method for quickly assessing the quality of superconducting detector wafers through optical imaging, reducing testing time and improving defect detection accuracy.

Contribution

It introduces a novel image analysis pipeline that predicts detector performance and identifies defects before cryogenic testing, enhancing efficiency for large detector arrays.

Findings

Detection accuracy of 98.6% on simulated defect images

Effective identification of visual defects correlating with detector performance

Successful application on prototype microwave kinetic inductance detectors

Abstract

The testing and quality assurance of cryogenic superconducting detectors is a time- and labor-intensive process. As experiments deploy increasingly larger arrays of detectors, new methods are needed for performing this testing quickly. Here, we propose a process for flagging under-performing detector wafers before they are ever tested cryogenically. Detectors are imaged under an optical microscope, and computer vision techniques are used to analyze the images, searching for visual defects and other predictors of poor performance. Pipeline performance is verified via a suite of images with simulated defects, yielding a detection accuracy of 98.6%. Lastly, results from running the pipeline on prototype microwave kinetic inductance detectors from the planned SPT-3G+ experiment are presented.