  While Cadmium Telluride (CdTe) excels in terms of photon radiation absorption
properties and outperforms silicon (Si) in this respect, the crystal growth,
characterization and processing into a radiation detector is much more
complicated. Additionally, large concentrations of extended crystallographic
defects, such as grain boundaries, twins, and tellurium (Te) inclusions, vary
from crystal to crystal and can reduce the spectroscopic performance of the
processed detector. A quality assessment of the material prior to the complex
fabrication process is therefore crucial. To locate the Te-defects, we scan the
crystals with infrared microscopy (IRM) in different layers, obtaining a 3D
view of the defect distribution. This provides us with important information on
the defect density and locations of Te inclusions, and thus a handle to assess
the quality of the material. For the classification of defects in the large
amount of IRM image data, a convolutional neural network is employed. From the
post-processed and analysed IRM data, 3D defect maps of the CdTe crystals are
created, which make different patterns of defect agglomerations inside the
crystals visible. In total, more than 100 crystals were scanned with the
current IRM setup. In this paper, we compare two crystal batches, each
consisting of 12 samples. We find significant differences in the defect
distributions of the crystals.
