This work introduces a comprehensive approach utilizing data-driven methods
to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD)
reactors and the interplay of physical mechanism that dominate in each one of
them. Through this work, we address three key objectives. Firstly, our
methodology relies on process outcomes, derived by a detailed CFD model, to
identify clusters of "outcomes" corresponding to distinct process regimes,
wherein the relative influence of input variables undergoes notable shifts.
This phenomenon is experimentally validated through Arrhenius plot analysis,
affirming the efficacy of our approach. Secondly, we demonstrate the
development of an efficient surrogate model, based on Polynomial Chaos
Expansion (PCE), that maintains accuracy, facilitating streamlined
computational analyses. Finally, as a result of PCE, sensitivity analysis is
made possible by means of Sobol' indices, that quantify the impact of process
inputs across identified regimes. The insights gained from our analysis
contribute to the formulation of hypotheses regarding phenomena occurring
beyond the transition regime. Notably, the significance of temperature even in
the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests
activation of gas phase reactions at elevated temperatures. Importantly, our
proposed methods yield insights that align with experimental observations and
theoretical principles, aiding decision-making in process design and
optimization. By circumventing the need for costly and time-consuming
experiments, our approach offers a pragmatic pathway towards enhanced process
efficiency. Moreover, this study underscores the potential of data-driven
computational methods for innovating reactor design paradigms.

本研究介绍了一种综合方法，利用数据驱动方法来阐明化学气相沉积（CVD）反应器中的沉积过程机制，并揭示了各个机制之间相互作用。该研究通过实验证明，采用流场分布（CFD）模型衍生的过程结果可以识别出对应于不同过程机制的 “结果” 簇，其中输入变量的相对影响发生显著转变。此外，通过多项式混沌展开（PCE）方法构建了一个高效的代理模型，该模型具有高准确性，方便了计算分析。最后，通过 Sobol' 指数实现了敏感性分析，量化了在已识别过程中的过程输入的影响。这项研究的揭示为后过渡区域发生的现象提供了假设，并强调了温度在扩散受限区域的重要性，即阿伦尼乌斯图表证明了在高温下激发了气相反应。我们提出的方法不仅具有与实验观察和理论原则相一致的洞察力，还有助于决策过程设计和优化。借助避免了昂贵费时的实验，我们的方法为提高过程效率提供了实用途径。此外，这项研究强调了基于数据驱动的计算方法在创新反应器设计范例方面的潜力。

发现沉积过程的规则：利用无监督学习进行过程洞察、代理模型和敏感性分析

Discovering deposition process regimes: leveraging unsupervised learning  for process insights, surrogate modeling, and sensitivity analysis

This study introduces a machine learning framework tailored to large-scale
industrial processes characterized by a plethora of numerical and categorical
inputs. The framework aims to (i) discern critical parameters influencing the
output and (ii) generate accurate out-of-sample qualitative and quantitative
predictions of production outcomes. Specifically, we address the pivotal
question of the significance of each input in shaping the process outcome,
using an industrial Chemical Vapor Deposition (CVD) process as an example. The
initial objective involves merging subject matter expertise and clustering
techniques exclusively on the process output, here, coating thickness
measurements at various positions in the reactor. This approach identifies
groups of production runs that share similar qualitative characteristics, such
as film mean thickness and standard deviation. In particular, the differences
of the outcomes represented by the different clusters can be attributed to
differences in specific inputs, indicating that these inputs are critical for
the production outcome. Leveraging this insight, we subsequently implement
supervised classification and regression methods using the identified critical
process inputs. The proposed methodology proves to be valuable in scenarios
with a multitude of inputs and insufficient data for the direct application of
deep learning techniques, providing meaningful insights into the underlying
processes.

该研究介绍了一个针对大规模工业过程的机器学习框架，旨在识别影响输出的关键参数，并生成精确的离样品定性和定量预测。该框架使用化学气相沉积作为示例，通过合并专业知识和聚类技术，确定了关键过程输入，并证明在输入众多、数据不足以直接应用深度学习技术的情况下具有价值，为底层过程提供有意义的洞察。

整合监督学习和无监督学习方法揭示关键过程输入

Integrating supervised and unsupervised learning approaches to unveil  critical process inputs

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。