Explainable machine learning for the analysis of transport phenomena in top-seeded solution growth of SiC single crystal-Reference-Cited by-同舟云学术

Explainable machine learning for the analysis of transport phenomena in top-seeded solution growth of SiC single crystal

Published:2021 Issue:1 Volume:16 Page:JTST0009-JTST0009
ISSN:1880-5566
Container-title:Journal of Thermal Science and Technology
language:en
Short-container-title:JTST

Author:

TAKEHARA Yuto¹,SEKIMOTO Atsushi¹,OKANO Yasunori¹,UJIHARA Toru²,DOST Sadik³

Affiliation:

1. Department of Materials Engineering Science, Osaka University

2. Department of Crystalline Materials Science, Nagoya University

3. Crystal Growth Laboratory, University of Victoria

Publisher

Japan Society of Mechanical Engineers

Subject

Engineering (miscellaneous),Instrumentation,General Materials Science,Atomic and Molecular Physics, and Optics

Link

https://www.jstage.jst.go.jp/article/jtst/16/1/16_2021jtst0009/_pdf

Reference11 articles.

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3. Durand F., Duby JC., Carbon solubility in solid and liquid silicon – A review with reference to eutectic equilibrium, Journal of Phase Equilibria, Vol.20, No.1 (1999), pp.61–63.

4. Durand F., Duby JC., Coupled heat transfer and fluid dynamics modeling of high-temperature SiC solution growth. Journal of Crystal Growth, Vol.312, No.2 (2010), pp.155–163.

5. Horiuchi T., Wang L., Sekimoto A., Okano Y., Ujihara T., Dost S., Adjoint-based sensitivity analysis for the optimal crucible temperature profile in the RF-Heating TSSG-SiC crystal growth process, Journal of Crystal Growth, Vol.517 (2019), pp.59–63.

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