| Autor: |
Zhuorui Tang, Shibo Zhao, Jian Li, Yuanhui Zuo, Jing Tian, Hongyu Tang, Jiajie Fan, Guoqi Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Case Studies in Thermal Engineering, Vol 59, Iss , Pp 104507- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2214-157X |
| DOI: |
10.1016/j.csite.2024.104507 |
| Popis: |
This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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