Process refinement to consistently produce high-quality material over a large
area of the grown crystal, enabling various applications from optics crystals
to quantum detectors, has long been a goal for diamond growth. Machine learning
offers a promising path toward this goal, but faces challenges such as the
complexity of features within datasets, their time-dependency, and the volume
of data produced per growth run. Accurate spatial feature extraction from image
to image for real-time monitoring of diamond growth is crucial yet complicated
due to the low-volume and high feature complexity nature of the datasets. This
paper compares various traditional and machine learning-driven approaches for
feature extraction in the diamond growth domain, proposing a novel deep
learning-driven semantic segmentation approach to isolate and classify accurate
pixel masks of geometric features like diamond, pocket holder, and background,
along with their derivative features based on shape and size. Using an
annotation-focused human-in-the-loop software architecture for 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 labeling time and cost. Deep
learning algorithms prove highly efficient in accurately learning complex
representations from datasets with many features. Our top-performing model,
based on the DeeplabV3plus architecture, achieves outstanding accuracy in
classifying features of interest, with accuracies of 96.31% for pocket holder,
98.60% for diamond top, and 91.64% for diamond side features.

通过比较传统方法和机器学习驱动方法，该论文提出了一种新颖的深度学习驱动的语义分割方法，用于钻石生长领域的特征提取，实现了对钻石、挡板和背景等几何特征的像素掩膜的分离和分类，通过注重标注的人在环软件架构实现数据集的训练，结合主动学习、数据增强和模型辅助标注的模块，有效提高了标注的准确性并显著降低了标注的时间和成本，其中 DeeplabV3plus 架构的模型在准确分类感兴趣特征方面的性能优秀，对挡板的准确率达到 96.31%，对钻石顶部的准确率达到 98.60%，对钻石侧面的准确率达到 91.64%。

以人工智能引导的特征分割技术对单晶金刚石生长中的特征进行建模

AI-Guided Feature Segmentation Techniques to Model Features from Single  Crystal Diamond Growth

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.

通过利用原位光学图像识别可见的宏观缺陷状态特征，本文详细介绍了利用语义分割方法进行缺陷分割的方法，并采用基于 YOLOV3 和 DeeplabV3plus 架构的深度学习算法，在钻石生长的过程中取得了很好的准确性。