Numerical simulation of InGaSb crystals growth under microgravity onboard the international space station

Jin, Xin; Mirsandi, H.; Yamamoto, Takuya; Takagi, Yasunari; Okano, Yasunori; Inatomi, Yuko; Hayakawa, Y.; Dost, S.

doi:10.7567/jjapcp.4.011107

Proc. 14th Int. Conf. On Global Research and Education, Inter-Academia 2015 2016

DOI: 10.7567/jjapcp.4.011107

|View full text |Cite

Numerical simulation of InGaSb crystals growth under microgravity onboard the international space station

Xin Jin¹,

H. Mirsandi²,

Takuya Yamamoto³

et al.

Abstract: In x Ga 1-x Sb bulk crystal was grown using a GaSb(seed)/InSb/GaSb(feed) sandwich-structured system onboard the International Space Station (ISS). In order to investigate the transport phenomena especially in terms of interface shapes and dissolution heights, the dissolution process was simulated under a micro-gravity level of the ISS. Simulation results showed that the seed/melt interface was concave towards the seed due to the temperature distribution of the system. This prediction is in good agreement with … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 12 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning

Ghritli¹,

Okano²,

Inatomi³

et al. 2022

Jpn. J. Appl. Phys.

Self Cite

View full text Add to dashboard Cite

Growth of high quality InGaSb crystals by Vertical Gradient Freezing (VGF) under microgravity was numerically simulated. Machine learning tools were used to optimize the growth conditions. The study focuses on controlling the growth interface shape which directly affects the quality and homogeneity of the grown crystals. Initially, Bayesian optimization was utilized to search for the most favorable growth conditions that promote a desirable flatter growth interface shape. Afterwards, a reinforcement learning model was developed. The system was subjected to a lower temperature gradient near the feed crystal and to crucible rotation with a rate ranging according to the obtained optimal strategy. Results showed that the interface deformation is considerably reduced, and a flatter growth interface could be maintained. The growth rate and solute concentration uniformity were also improved. This adaptive control recipe proves to hold a great potential in the continuous and rapid optimization of other crystal growth processes.

show abstract

Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning

Ghritli¹,

Okano²,

Inatomi³

et al. 2022

Jpn. J. Appl. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Numerical simulation of InGaSb crystals growth under microgravity onboard the international space station

Cited by 1 publication

References 12 publications

Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning

Control of growth interface shape during InGaSb growth by vertical gradient freezing under microgravity, and optimization using machine learning

Contact Info

Product

Resources

About