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.

翻译：本研究提出了一种综合利用数据驱动方法的系统性方案，以阐明化学气相沉积（CVD）反应器中的沉积过程机制及各机制中主导物理过程的相互作用。通过此项工作，我们实现了三个关键目标。首先，我们的方法基于由详细计算流体动力学（CFD）模型导出的过程结果，识别出对应于不同过程机制的“结果”聚类，其中输入变量的相对影响发生显著变化。这一现象通过阿伦尼乌斯曲线分析得到实验验证，从而证实了我们方法的有效性。其次，我们展示了基于多项式混沌展开（PCE）构建高效代理模型的过程，该模型在保持精度的同时实现了简化的计算分析。最后，得益于PCE，我们能够借助索博尔指数进行敏感性分析，从而量化过程输入在已识别机制中的影响程度。分析所得见解有助于形成关于过渡机制之外发生现象的假设。值得注意的是，阿伦尼乌斯曲线证实了即使在扩散限制机制中温度仍具有显著影响，这表明在高温下气相反应被激活。重要的是，我们提出的方法所获得的见解与实验观察和理论原理相一致，有助于过程设计与优化的决策制定。通过避免成本高昂且耗时的实验，我们的方法为提升过程效率提供了一条实用路径。此外，本研究强调了数据驱动计算方法在革新反应器设计范式方面的潜力。