Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

doi:10.1016/j.cjche.2020.03.035

中国化学工程学报 ›› 2020, Vol. 28 ›› Issue (6): 1733-1743.DOI: 10.1016/j.cjche.2020.03.035

• Materials and Product Engineering • 上一篇下一篇

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

Kang Guan¹, Yong Gao², Qingfeng Zeng^2,3, Xingang Luan², Yi Zhang², Laifei Cheng², Jianqing Wu¹, Zhenya Lu¹

1 School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China;
2 Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3 MSEA International Institute for Materials Genome, Gu'an 065500, China

收稿日期:2019-11-01 修回日期:2020-03-05 出版日期:2020-06-28 发布日期:2020-07-29
通讯作者: Jianqing Wu, Zhenya Lu
基金资助:
We thank the National Key R&D Program of China (Grants No. 2017YFB0703200), National Natural Science Foundation of China (Grants Nos. 51702100, 51972268) and China Postdoctoral Science Foundation (Grants No. 2018M643075) for financial support.

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

Kang Guan¹, Yong Gao², Qingfeng Zeng^2,3, Xingang Luan², Yi Zhang², Laifei Cheng², Jianqing Wu¹, Zhenya Lu¹

1 School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China;
2 Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3 MSEA International Institute for Materials Genome, Gu'an 065500, China

Received:2019-11-01 Revised:2020-03-05 Online:2020-06-28 Published:2020-07-29
Contact: Jianqing Wu, Zhenya Lu
Supported by:
We thank the National Key R&D Program of China (Grants No. 2017YFB0703200), National Natural Science Foundation of China (Grants Nos. 51702100, 51972268) and China Postdoctoral Science Foundation (Grants No. 2018M643075) for financial support.

摘要/Abstract

摘要： The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition (LPCVD) or low-pressure chemical vapor infiltration (LPCVI). In the field of ceramic matrix composites (CMCs), methyltrichlorosilane (CH₃SiCl₃, MTS) is the most widely used source gas system for SiC, because stoichiometric SiC deposit can be facilitated at 900℃-1300℃. However, the reliability and accuracy of existing numerical models for these processing conditions are rarely reported. In this study, a comprehensive transport model was coupled with gas-phase and surface kinetics. The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species. The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method. Surface kinetics were then proposed and shown to reproduce the experimental results. The proposed simulation strategy can be used for different material systems.

关键词: Chemical vapor deposition, MTS/H₂, Gas-phase and surface kinetics, Extreme learning machine method, Numerical model

Abstract: The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition (LPCVD) or low-pressure chemical vapor infiltration (LPCVI). In the field of ceramic matrix composites (CMCs), methyltrichlorosilane (CH₃SiCl₃, MTS) is the most widely used source gas system for SiC, because stoichiometric SiC deposit can be facilitated at 900℃-1300℃. However, the reliability and accuracy of existing numerical models for these processing conditions are rarely reported. In this study, a comprehensive transport model was coupled with gas-phase and surface kinetics. The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species. The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method. Surface kinetics were then proposed and shown to reproduce the experimental results. The proposed simulation strategy can be used for different material systems.

Key words: Chemical vapor deposition, MTS/H₂, Gas-phase and surface kinetics, Extreme learning machine method, Numerical model

Kang Guan, Yong Gao, Qingfeng Zeng, Xingang Luan, Yi Zhang, Laifei Cheng, Jianqing Wu, Zhenya Lu. Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane[J]. 中国化学工程学报, 2020, 28(6): 1733-1743.

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Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

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