المؤلفون: Kyoka Hara, Takuto Kojima, Kentaro Kutsukake, Hiroaki Kudo, Noritaka Usami

المصدر: APL Machine Learning, Vol 1, Iss 3, Pp 036106-036106-9 (2023)

مصطلحات موضوعية: Physics, QC1-999, Electronic computers. Computer science, QA75.5-76.95

الوصف: We propose a machine learning-based technique to address the crystallographic characteristics responsible for the generation of crystal defects. A convolutional neural network was trained with pairs of optical images that display the characteristics of the crystal and photoluminescence images that show the distributions of crystal defects. The model was trained to predict the existence of crystal defects at the center pixel of the given image from its optical features. Prediction accuracy and separability were enhanced by feeding three-dimensional data and data augmentation. The prediction was successful with a high area under the curve of over 0.9 in a receiver operating characteristic curve. Likelihood maps showing the distributions of the predicted defects are in good resemblance with the correct distributions. Using the trained model, we visualized the most important regions to the predicted class by gradient-based class activation mapping. The extracted regions were found to contain mostly particular grains where the grain boundaries changed greatly due to crystal growth and clusters of small grains. This technique is beneficial in providing a rapid and statistical analysis of various crystal characteristics because the features of optical images are often complex and difficult to interpret. The interpretations can help us understand the physics of crystal growth and the effects of crystallographic characteristics on the generation of detrimental defects. We believe that this technique will contribute to the development of a better fabrication process for high-performance multicrystalline materials.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2770-9019Test

الوصول الحر: https://doaj.org/article/3bc8d3aaa6ee42c9a52e09cbe5383f9bTest

المؤلفون: Kyoka Hara, Takuto Kojima, Kentaro Kutsukake, Hiroaki Kudo, Noritaka Usami

المصدر: APL Machine Learning, Vol 1, Iss 2, Pp 026113-026113-9 (2023)

مصطلحات موضوعية: Physics, QC1-999, Electronic computers. Computer science, QA75.5-76.95

الوصف: We established a rapid, low-cost, and accurate technique to measure crystallographic orientations in multicrystalline materials by optical images and machine learning. A long short-term memory neural network was trained with pairs of light reflection patterns and the correct orientations of each grain, successfully predicting orientation with an error median of 8.61°. The model was improved by diverse data taken from various incident light angles and by data augmentation. When trained on different incident angles, the model was capable of estimating different orientations. This is related to the geometrical configuration of the incident light angles and surface facets of the crystal. The failure in certain orientations is thought to be complemented by supplementary data taken from different incident angles. Combining data from multiple incident angles, we acquired an error median of 4.35°. Data augmentation was successfully performed, reducing error by an additional 35%. This technique can provide the crystallographic orientations of a 15 × 15 cm2 sized wafer in less than 8 min, while baseline techniques such as electron backscatter diffraction and Laue scanner may take more than 10 h. The rapid and accurate measurement can accelerate data collection for full-sized ingots, helping us gain a comprehensive understanding of crystal growth. We believe that our technique will contribute to controlling crystalline structure for the fabrication of high-performance materials.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2770-9019Test

الوصول الحر: https://doaj.org/article/d2c6842e862a418d8c584913807eccabTest

المؤلفون: Hiroaki Kudo, Shaoxiang Dang, Tetsuya Matsumoto, Yoshinori Takeuchi

المصدر: IEEJ Transactions on Electronics, Information and Systems. 2022, 142(6):643

المؤلفون: Hiroaki Kudo, Kentaro Kutsukake, Kyoka Hara, Noritaka Usami, Takuto Kojima, Tetsuya Matsumoto, 原 京花, 宇佐美 徳隆, 小島 拓人, 工藤 博章, 松本 哲也, 沓掛 健太朗

المصدر: Proceedings of the Annual Meeting of the Japan Photovoltaic Society. 2022, :48

المؤلفون: Hiroaki Kudo, Kentaro Kutsukake, Noritaka Usami, Takuto Kojima, Tetsuya Matsumoto, 宇佐美 徳隆, 小島 拓人, 工藤 博章, 松本 哲也, 沓掛 健太朗

المصدر: Proceedings of the Annual Meeting of the Japan Photovoltaic Society. 2022, :14

المؤلفون: Hiroaki Kudo, Kentaro Kutsukake, Kyoka Hara, Manickam Srinivasan, Noritaka Usami, Perumalsamy Ramasamy, Takuto Kojima, Thamotharan Keerthivasan, タモタラン ケルチバサン, ペルマルザミー ラマザミー, マニッカム スリニバサン, 原 京花, 宇佐美 徳隆, 小島 拓人, 工藤 博章, 沓掛 健太朗

المصدر: Proceedings of the Annual Meeting of the Japan Photovoltaic Society. 2021, :125

المؤلفون: Hiroaki Kudo, Hiroyuki Tanaka, Kenta Yamakoshi, Kentaro Kutsukake, Noritaka Usami, Takuto Kojima, Yutaka Ohno, 大野 裕, 宇佐美 徳隆, 小島 拓人, 山腰 健太, 工藤 博章, 沓掛 健太朗, 田中 博之

المصدر: Proceedings of the Annual Meeting of the Japan Photovoltaic Society. 2021, :52

المؤلفون: Hiroaki Kudo, Kentaro Kutsukake, Noritaka Usami, Takuto Kojima, Tetsuya Matsumoto, 宇佐美 徳隆, 小島 拓人, 工藤 博章, 松本 哲也, 沓掛 健太朗

المصدر: Proceedings of the Annual Meeting of the Japan Photovoltaic Society. 2021, :50

المؤلفون: Hiroaki KUDO, Miho SHINOHARA, Mitsuho YAMADA, Shinya MOCHIDUKI, Yukina TAMURA

المصدر: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2021, E104.A(6):897

المؤلفون: Hiroaki KUDO, Kentaro KUTSUKAKE, Noritaka USAMI, Tetsuya MATSUMOTO

المصدر: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2021, E104.A(6):857