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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Bibliografiske detaljer

Principais autores:	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:	Artigo
Sprog:	Inglês
Udgivet:	Elsevier 2024-11-01
Serier:	Chemical Engineering Journal Advances
Fag:	Arrhenius plot Fe CVD reactor Hierarchical clustering Polynomial chaos expansion Uncertainty quantification Sensitivity analysis
Online adgang:	http://www.sciencedirect.com/science/article/pii/S266682112400084X
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