TY - JOUR
T1 - Estimation of the diffusion coefficient of GaSb in InSb melt using Bayesian optimization and the ISS experimental results
AU - Ghritli, Rachid
AU - Okano, Yasunori
AU - Inatomi, Yuko
AU - Sekimoto, Atsushi
AU - Dost, Sadik
N1 - Funding Information:
This work was financially supported by Grant-in-Aid for Science Research (B) (JSPS KAKENHI, JP19H02491) and the computations were carried out using the computational resources of the Institute for Information Management and Communication, Kyoto University, Japan. The authors also thank Mr. Kazuya O. for his valuable work in the related research.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - The diffusion coefficient of GaSb in InSb melt was estimated by using the microgravity experimental results performed onboard the International Space Station (ISS), where the effect of natural convection is minimized. We numerically investigated the transport phenomena during InGaSb crystals growth via a vertical gradient freezing method and it was deduced that the growth rate is dominated by the solute diffusion in the melt. Furthermore, the crystal growth rate was well explained by adopting a diffusion coefficient with function of GaSb concentration, obtained by analyzing the available experimental results and utilizing machine learning and Bayesian optimization methods. These results also contribute to further understanding of crystal growth on the ground.
AB - The diffusion coefficient of GaSb in InSb melt was estimated by using the microgravity experimental results performed onboard the International Space Station (ISS), where the effect of natural convection is minimized. We numerically investigated the transport phenomena during InGaSb crystals growth via a vertical gradient freezing method and it was deduced that the growth rate is dominated by the solute diffusion in the melt. Furthermore, the crystal growth rate was well explained by adopting a diffusion coefficient with function of GaSb concentration, obtained by analyzing the available experimental results and utilizing machine learning and Bayesian optimization methods. These results also contribute to further understanding of crystal growth on the ground.
KW - A1. Computer simulation
KW - A1. Diffusion
KW - A1. Mass transfer
KW - A2. Gradient freeze technique
KW - A2. Microgravity conditions
KW - B2. Semiconducting III-V materials
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U2 - 10.1016/j.jcrysgro.2021.126280
DO - 10.1016/j.jcrysgro.2021.126280
M3 - Article
AN - SCOPUS:85112557828
SN - 0022-0248
VL - 573
JO - Journal of Crystal Growth
JF - Journal of Crystal Growth
M1 - 126280
ER -