  Cryogenic characterization of transition-edge sensor (TES) bolometers is a
time- and labor-intensive process. As new experiments deploy larger and larger
arrays of TES bolometers, the testing process will become more of a bottleneck.
Thus it is desirable to develop a method for evaluating detector performance at
room temperature. One possibility is using machine learning to correlate
detectors' visual appearance with their cryogenic properties. Here, we use
three engineering-grade TES bolometer wafers from the production cycle for
SPT-3G, the current receiver on the South Pole Telescope, to train and test
such an algorithm. High-resolution images of these TES bolometers were captured
and relevant features were calculated from the images. Cryogenic performance
metrics, including a detector's ability to tune and superconducting parameters
such as normal resistance, critical temperature, and transition width, were
also measured. A random forest algorithm was trained to predict these
performance metrics from the visual features. Analysis of the images proved
highly successful. While the ability of the random forest algorithm to predict
cryogenic features was limited with the chosen set of input image features, it
is possible that an increase in data volume or the addition of more image
features will solve this problem.
