  From a process development perspective, diamond growth via chemical vapor
deposition has made significant strides. However, challenges persist in
achieving high quality and large-area material production. These difficulties
include controlling conditions to maintain uniform growth rates for the entire
growth surface. As growth progresses, various factors or defect states emerge,
altering the uniform conditions. These changes affect the growth rate and
result in the formation of crystalline defects at the microscale. However,
there is a distinct lack of methods to identify these defect states and their
geometry using images taken during the growth process. This paper details
seminal work on defect segmentation pipeline using in-situ optical images to
identify features that indicate defective states that are visible at the
macroscale. Using a semantic segmentation approach as applied in our previous
work, these defect states and corresponding derivative features are isolated
and classified by their pixel masks. Using an annotation focused
human-in-the-loop software architecture to produce training datasets, with
modules for selective data labeling using active learning, data augmentations,
and model-assisted labeling, our approach achieves effective annotation
accuracy and drastically reduces the time and cost of labeling by orders of
magnitude. On the model development front, we found that deep learning-based
algorithms are the most efficient. They can accurately learn complex
representations from feature-rich datasets. Our best-performing model, based on
the YOLOV3 and DeeplabV3plus architectures, achieved excellent accuracy for
specific features of interest. Specifically, it reached 93.35% accuracy for
center defects, 92.83% for polycrystalline defects, and 91.98% for edge
defects.
