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

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

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,...

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Bibliographic Details
Main Authors: Geremy Loachamín-Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P.A. Bordas, Andreas G. Boudouvis
Format: Article
Language:English
Published: Elsevier 2024-11-01
Series:Chemical Engineering Journal Advances
Subjects:
Arrhenius plot
Fe CVD reactor
Hierarchical clustering
Polynomial chaos expansion
Uncertainty quantification
Sensitivity analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S266682112400084X
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http://www.sciencedirect.com/science/article/pii/S266682112400084X

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