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

被引:0
|
作者
Kang Guan [1 ]
Yong Gao [2 ]
Qingfeng Zeng [2 ,3 ]
Xingang Luan [2 ]
Yi Zhang [2 ]
Laifei Cheng [2 ]
Jianqing Wu [1 ]
Zhenya Lu [1 ]
机构
[1] School of Materials Science and Engineering, South China University of Technology
[2] MSEA International Institute for Materials Genome
[3] Science and Technology on Thermostructural Composite Materials Laboratory, School of Materials Science and Engineering, Northwestern Polytechnical University
基金
国家重点研发计划; 中国国家自然科学基金; 中国博士后科学基金;
关键词
Chemical vapor deposition; MTS/H2; Gas-phase and surface kinetics; Extreme learning machine method; Numerical model;
D O I
暂无
中图分类号
TB33 [复合材料];
学科分类号
0805 ; 080502 ;
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(CH3 SiCl3, MTS) is the most widely used source gas system for SiC, because stoichiometric SiC deposit can be facilitated at 900 °C–1300 °C. 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.
引用
收藏
页码:1733 / 1743
页数:11
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