  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.
