Machine learning-based modeling and operation for ALD of SiO2 thin-films using data from a multiscale CFD simulation

[Display omitted] •Multiscale computational fluid dynamics (CFD) modeling of ALD reactor.•Machine-learning modeling using multiscale CFD model data.•Use of machine learning model to optimize ALD cycle time.•Significant reduction of ALD cycle time versus fixed-time deposition. Atomic layer deposition...

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Veröffentlicht in:	Chemical engineering research & design Jg. 151; S. 131 - 145
Hauptverfasser:	Ding, Yangyao, Zhang, Yichi, Ren, Yi Ming, Orkoulas, Gerassimos, Christofides, Panagiotis D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Rugby Elsevier B.V 01.11.2019 Elsevier Science Ltd
Schlagworte:	ALD operation Artificial neural networks Atomic layer deposition Atomic layer epitaxy Computational efficiency Computational fluid dynamics Computational fluid dynamics modeling Computer simulation Computing time Feasibility Film growth First principles Flow velocity High aspect ratio Kinetic Monte Carlo modeling Learning theory Machine learning Machine learning modeling Mathematical models Multiscale modeling Neural networks Optimization Precursors Process parameters Real time Silicon dioxide Thin films Computational fluid dynamics modeling ALD operation Kinetic Monte Carlo modeling Atomic layer deposition Multiscale modeling Machine learning modeling
ISSN:	0263-8762, 1744-3563
Online-Zugang:	Volltext
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