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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Vydáno v:	Chemical engineering research & design Ročník 151; s. 131 - 145
Hlavní autoři:	Ding, Yangyao, Zhang, Yichi, Ren, Yi Ming, Orkoulas, Gerassimos, Christofides, Panagiotis D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Rugby Elsevier B.V 01.11.2019 Elsevier Science Ltd
Témata:	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
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Shrnutí:	[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 (ALD) is a widely utilized deposition technology in the semiconductor industry due to its superior ability to generate highly conformal films and to deposit materials into high aspect-ratio geometric structures. However, ALD experiments remain expensive and time-consuming, and the existing first-principles based models have not yet been able to provide solutions to key process outputs that are computationally efficient, which is necessary for on-line optimization and real-time control. In this work, a multiscale data-driven model is proposed and developed to capture the macroscopic process domain dynamics with a linear parameter varying model, and to characterize the microscopic domain film growth dynamics with a feed-forward artificial neural network (ANN) model. The multiscale data-driven model predicts the transient deposition rate from the following four key process operating parameters that can be manipulated, measured or estimated by process engineers: precursor feed flow rate, operating pressure, surface heating, and transient film coverage. Our results demonstrate that the multiscale data-driven model can efficiently characterize the transient input-output relationship for the SiO2 thermal ALD process using bis(tertiary-butylamino)silane (BTBAS) as the Si precursor. The multiscale data-driven model successfully reduces the computational time from 0.6 to 1.2h for each time step, which is required for the first-principles based multiscale computational fluid dynamics (CFD) model, to less than 0.1s, making its real-time usage feasible. The developed data-driven modeling methodology can be further generalized and used for other thermal ALD or similar deposition systems, which will greatly enhance the feasibility of industrial manufacturing processes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0263-8762 1744-3563
DOI:	10.1016/j.cherd.2019.09.005