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Machine learning-based run-to-run control of a spatial thermal atomic layer etching reactor. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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2
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Yun S, Tom M, Orkoulas G, Christofides PD. Multiscale computational fluid dynamics modeling of spatial thermal atomic layer etching. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107861] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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3
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Yun S, Tom M, Luo J, Orkoulas G, Christofides PD. Microscopic and data-driven modeling and operation of thermal atomic layer etching of aluminum oxide thin films. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.10.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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4
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Zhuang L, Corkery P, Lee DT, Lee S, Kooshkbaghi M, Xu Z, Dai G, Kevrekidis IG, Tsapatsis M. Numerical simulation of atomic layer deposition for thin deposit formation in a mesoporous substrate. AIChE J 2021. [DOI: 10.1002/aic.17305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Liwei Zhuang
- School of Chemical Engineering East China University of Science and Technology Shanghai China
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
- Institute for NanoBio Technology Johns Hopkins University Baltimore Maryland USA
| | - Peter Corkery
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
- Institute for NanoBio Technology Johns Hopkins University Baltimore Maryland USA
| | - Dennis T. Lee
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
- Institute for NanoBio Technology Johns Hopkins University Baltimore Maryland USA
| | - Seungjoon Lee
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Mahdi Kooshkbaghi
- Program in Applied and Computational Mathematics Princeton University Princeton New Jersey USA
| | - Zhen‐liang Xu
- School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Gance Dai
- School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Ioannis G. Kevrekidis
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
| | - Michael Tsapatsis
- Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland USA
- Institute for NanoBio Technology Johns Hopkins University Baltimore Maryland USA
- Applied Physics Laboratory Johns Hopkins University Laurel Maryland USA
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Yun S, Ding Y, Zhang Y, Christofides PD. Integration of feedback control and run-to-run control for plasma enhanced atomic layer deposition of hafnium oxide thin films. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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6
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Machine learning-based modeling and operation of plasma-enhanced atomic layer deposition of hafnium oxide thin films. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107148] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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Zhang Y, Ding Y, Christofides PD. Multiscale computational fluid dynamics modeling and reactor design of plasma-enhanced atomic layer deposition. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107066] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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9
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Ding Y, Zhang Y, Orkoulas G, Christofides PD. Microscopic modeling and optimal operation of plasma enhanced atomic layer deposition. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.05.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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10
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Integrating Feedback Control and Run-to-Run Control in Multi-Wafer Thermal Atomic Layer Deposition of Thin Films. Processes (Basel) 2019. [DOI: 10.3390/pr8010018] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
There is currently a lack of understanding of the deposition profile in a batch atomic layer deposition (ALD) process. Also, no on-line control scheme has been proposed to resolve the prevalent disturbances. Motivated by this, we develop a computational fluid dynamics (CFD) model and an integrated online run-to-run and feedback control scheme. Specifically, we analyze a furnace reactor for a SiO2 thin-film ALD with BTBAS and ozone as precursors. Initially, a high-fidelity 2D axisymmetric multiscale CFD model is developed using ANSYS Fluent for the gas-phase characterization and the surface thin-film deposition, based on a kinetic Monte-Carlo (kMC) model database. To deal with the disturbance during reactor operation, a proportional integral (PI) control scheme is adopted, which manipulates the inlet precursor concentration to drive the precursor partial pressure to the set-point, ensuring the complete substrate coverage. Additionally, the CFD model is utilized to investigate a wide range of operating conditions, and a regression model is developed to describe the relationship between the half-cycle time and the feed flow rate. A run-to-run (R2R) control scheme using an exponentially weighted moving average (EWMA) strategy is developed to regulate the half-cycle time for the furnace ALD process between batches.
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Kimaev G, Ricardez-Sandoval LA. Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks. Chem Eng Sci 2019. [DOI: 10.1016/j.ces.2019.07.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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12
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Machine learning-based modeling and operation for ALD of SiO2 thin-films using data from a multiscale CFD simulation. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.09.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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13
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Multiscale computational fluid dynamics modeling of thermal atomic layer deposition with application to chamber design. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.05.049] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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