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Park H, Choi H, Shin S, Park BK, Kang K, Ryu JY, Eom T, Chung TM. Evaluation of tin nitride (Sn 3N 4) via atomic layer deposition using novel volatile Sn precursors. Dalton Trans 2023; 52:15033-15042. [PMID: 37812132 DOI: 10.1039/d3dt02138f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Novel Sn precursors, Sn(tbip)2, Sn(tbtp)2, and Sn(tbta)2, were synthesized and characterized using various analytical techniques and density functional theory calculations. These precursors contained cyclic amine ligands derived from iminopyrrolidine. X-ray crystallography revealed the formation of monomeric SnL2 with distorted seesaw geometry. Thermogravimetric analysis demonstrated the exceptional volatility of all complexes. Sn(tbtp)2 showed the lowest residual weight of 2.7% at 265 °C. Sn3N4 thin films were successfully synthesized using Sn(tbtp)2 as the Sn precursor and NH3 plasma. The precursor exhibited ideal characteristics for atomic layer deposition, with a saturated growth per cycle value of 1.9 Å cy-1 and no need for incubation when the film was deposited at 150-225 °C. The indirect optical bandgap of the Sn3N4 film was approximately 1-1.2 eV, as determined through ultraviolet-visible spectroscopy. Therefore, this study suggests that the Sn3N4 thin films prepared using the newly synthesized Sn precursor are suitable for application in thin-film photovoltaic devices.
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Affiliation(s)
- Hyeonbin Park
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
- Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Heenang Choi
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Sunyoung Shin
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
| | - Bo Keun Park
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
- Department of Chemical Convergence Materials, University of Science and Technology (UST) 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
| | - Kibum Kang
- Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Ji Yeon Ryu
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
| | - Taeyong Eom
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
| | - Taek-Mo Chung
- Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
- Department of Chemical Convergence Materials, University of Science and Technology (UST) 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
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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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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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Sitapure N, Kwon JSI. Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.05.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Multiscale computational fluid dynamics modeling of thermal atomic layer etching: Application to chamber configuration design. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107757] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Molecular Dynamics of Atomic Layer Deposition: Sticking Coefficient Investigation. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042188] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This study focused on the atomic scale growth dynamics of amorphous Al2O3 films microscale structural relaxation. Classical Molecular Dynamics (MD) can not entirely model the challenging ALD dynamics due to the large timescales. The all-atom approach has rules based on deposition actions modelled MD relaxations that form as input to attain a single ALD cycle. MD relaxations are used to create a realistic equilibrium surface. This approach is fitting to this study as the investigation of the sticking coefficient is only at the first monolayer that includes the layering of a hydroxyl surface of alumina. The study provides insight between atomic-level numerical information and experimental measurements of the sticking coefficient related to the atomic layer deposition. The MD modeling was for the deposition of Al2O3, using trimethylaluminum (TMA) and water as precursors. The film thickness of 1.7 Å yields an initial sticking coefficient of TMA to be 4.257 × 10−3 determined from the slope of the leading front of the thickness profile at a substrate temperature of 573 K. This work adds to the knowledge of the kinetic nature of ALD at the atomic level. It provides quantitative information on the sticking coefficient during ALD.
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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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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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