Faculty Profiles - HARADA Shunta

Kokubo Nobuhiko, Tsunooka Yosuke, Fujie Fumihiro, Ohara Junji, Onda Shoichi, Yamada Hisashi, Shimizu Mitsuaki, Harada Shunta, Tagawa Miho, Ujihara Toru

JAPANESE JOURNAL OF APPLIED PHYSICS Vol. 58 ( SC ) 2019.6

SiC溶液成長による高品質化と機械学習の応用 Invited Reviewed

原田俊太，朱燦，角岡洋介，沓掛健太朗，宇治原徹

Vol. 14 ( 1 ) page： 13 - 18 2019.6

SiC溶液成長による高品質化と機械学習の応用 Invited Reviewed

原田俊太, 朱燦, 角岡洋介, 沓掛健太朗, 宇治原徹

Vol. 14 ( 1 ) page： 13 - 18 2019.6

Evaluation of basal plane dislocation behavior in the epitaxial layer on a 4H-SiC wafer fabricated by the solution growth method

Seki K.

Materials Science Forum Vol. 963 MSF page： 80 - 84 2019

Machine learning for SiC top-seeded solution growth - Prediction, optimization and visualization Reviewed

Toru Ujihara, Yosuke Tsunooka, Goki Hatasa, Can Zhu, Kentaro Kutsukake, Taka Narumi, Shunta Harada, Miho Tagawa

CS MANTECH 2019 - 2019 International Conference on Compound Semiconductor Manufacturing Technology, Digest of Papers 2019

Language：English Publishing type：Research paper (international conference proceedings)

© 2019 CS Mantech. All rights reserved. We are developing solution growth technique for high-quality SiC bulk crystal. In actual, we have achieved high-quality crystal with very-low-density of threading dislocations grown by controlling the surface morphology. In order to apply this technique to large-scale crystal growth, it is necessary to control supersaturation at growth surface, flow rate and flow direction of solvent in detail. However, there are many growth parameters which should be optimized. Simulation technique based on computational fluid dynamics (CFD) is often used. However, it is still difficult to optimize growth condition by utilizing simulation technique since the calculation speed of CFD simulation is not enough to optimize the growth conditions, exhaustively. In recent, informatics including machine learning is applied to various fields including materials science. In this study, we tried to apply machine learning to the analysis of the results of CFD. We could make the model to optimize the crystal growth parameters based on a neural network model. Using the model, the optimization time became 10000 times faster. This is just a trial of “Process Informatics”.

Scopus

Machine learning for SiC top-seeded solution growth - Prediction, optimization and visualization

Ujihara T.

CS MANTECH 2019 - 2019 International Conference on Compound Semiconductor Manufacturing Technology, Digest of Papers 2019

The Prediction Model of Crystal Growth Simulation Built by Machine Learning and Its Applications

UJIHARA Toru, TSUNOOKA Yosuke, HATASA Goki, KUTSUKAKE Kentaro, ISHIGURO Akio, MURAYAMA Kenta, NARUMI Taka, HARADA Shunta, TAGAWA Miho

Vacuum and Surface Science Vol. 62 ( 3 ) page： 136 - 140 2019

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japan Society of Vacuum and Surface Science

<p>The prediction model of the result of computed fluid dynamics simulation in SiC solution growth was constructed on neural network using machine learning. Utilizing the prediction model, we can optimize quickly crystal growth conditions. In addition, the real-time visualization system was also made using the prediction model.</p>

DOI： 10.1380/vss.62.136

CiNii Books

CiNii Research

The relationships between crystal orientation and morphology of electrode from a perspective of crystal growth

Ishikawa Kohei, Mitsuhashi Takato, Ito Yasumasa, Takeuchi Yukihisa, Harada Shunta, Tagawa Miho, Ujihara Toru

Journal of the Japanese Association for Crystal Growth Vol. 46 ( 1 ) page： 136-140 2019

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japanese Association for Crystal Growth

<p> Metal anodes are promising materials for rechargeable batteries because of the higher capacity. However, low cycle efficiency due to non-uniform deposition prevents them from realization. In this review, we report the crystal orientation dependence of the deposition shape of Li and Zn, which are known as attractive materials for anodes. The result shows that the both of Li and Zn morphology has crystal orientation dependence.</p>

DOI： 10.19009/jjacg.46-1-08

Improvement mechanism of sputtered AlN films by high-temperature annealing

Shiyu Xiao, Ryoya Suzuki, Hideto Miyake, Shunta Harada, Toru Ujihara

Journal of Crystal Growth Vol. 502 page： 41 - 44 2018.11

High-speed prediction of computational fluid dynamics simulation in crystal growth

Tsunooka Yosuke, Kokubo Nobuhiko, Hatasa Goki, Harada Shunta, Tagawa Miho, Ujihara Toru

CRYSTENGCOMM Vol. 20 ( 41 ) page： 6546 - 6550 2018.11

Determination of edge-component Burgers vector of threading dislocations in GaN crystal by using Raman mapping

Kokubo Nobuhiko, Tsunooka Yosuke, Fujie Fumihiro, Ohara Junji, Onda Shoichi, Yamada Hisashi, Shimizu Mitsuaki, Harada Shunta, Tagawa Miho, Ujihara Toru

APPLIED PHYSICS EXPRESS Vol. 11 ( 11 ) 2018.11

Observation of carrier recombination in single Shockley stacking faults and at partial dislocations in 4H-SiC

Kato Masashi, Katahira Shinya, Ichikawa Yoshihito, Harada Shunta, Kimoto Tsunenobu

JOURNAL OF APPLIED PHYSICS Vol. 124 ( 9 ) 2018.9

Classification and behavior of dislocations propagating from GaN substrate to epitaxial film

Inotsume Sho, Kokubo Nobuhiko, Yamada Hisashi, Onda Shoichi, Ohara Junji, Harada Shunta, Tagawa Miho, Ujihara Toru

JSAP Annual Meetings Extended Abstracts Vol. 2018.2 ( 0 ) page： 3248 - 3248 2018.9

In Situ Observation of Chiral Symmetry Breaking in NaClO3 Chiral Crystallization Realized by Thermoplasmonic Micro-Stirring

Niinomi Hiromasa, Sugiyama Teruki, Tagawa Miho, Harada Shunta, Ujihara Tom, Uda Satoshi, Miyamoto Katsuhiko, Omatsu Takashige

CRYSTAL GROWTH & DESIGN Vol. 18 ( 8 ) page： 4230 - 4239 2018.8

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Papers - HARADA Shunta