Print Email Facebook Twitter Optimizing the chemical vapor deposition process of 4H–SiC epitaxial layer growth with machine-learning-assisted multiphysics simulations Title Optimizing the chemical vapor deposition process of 4H–SiC epitaxial layer growth with machine-learning-assisted multiphysics simulations Author Tang, Zhuorui (Fudan University) Zhao, Shibo (Northeastern University) Li, Jian (Technology Innovation Institute of Jilin Province; Foshan University) Zuo, Yuanhui (Research Institute of Fudan University, Ningbo) Tian, Jing (Fudan University) Tang, Hongyu (Fudan University) Fan, J. (Fudan University) Zhang, Kouchi (TU Delft Electronic Components, Technology and Materials; Fudan University) Date 2024 Abstract 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. Subject 4H–SiC epitaxial layerCVDMachine learning modelMulti-physical simulationOptimization To reference this document use: http://resolver.tudelft.nl/uuid:8e3d396b-5809-47d8-85ee-47df4986f308 DOI https://doi.org/10.1016/j.csite.2024.104507 ISSN 2214-157X Source Case Studies in Thermal Engineering, 59 Part of collection Institutional Repository Document type journal article Rights © 2024 Zhuorui Tang, Shibo Zhao, Jian Li, Yuanhui Zuo, Jing Tian, Hongyu Tang, J. Fan, Kouchi Zhang Files PDF 1-s2.0-S2214157X24005380-main.pdf 12.46 MB Close viewer /islandora/object/uuid:8e3d396b-5809-47d8-85ee-47df4986f308/datastream/OBJ/view