Download all files (12.3MB)

コレクション

引用

説明:

We have developed an innovative design method for crystal growth furnaces and processes involving two-step optimization. The first step focuses on finding the ideal temperature transition around the crucible without assuming a specific crystal growth furnace. The design of the crystal growth furnace and process is then optimized to replicate the ideal temperature transition. We utilized a deep neural network model in each optimization step to substitute crystal growth simulation and genetic algorithm. A proof-of-concept optimization is performed for the directional solidification of a crystalline silicon ingot in a crucible. Since our method does not rely on a predetermined furnace, we can achieve more flexible temperature distribution transitions than conventional approaches by implementing adaptable temperature boundary conditions. This allows us to refine the design of the crystal growth furnace and process, which has significant potential to advance the production of a wide range of materials and improve materials production environments and equipment design.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Optimization, machine learning, genetic algorithm, crystal growth, process informatics

刊行年月日: 2025-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 5 issue. 1 2581358

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6191

公開URL: https://doi.org/10.1080/27660400.2025.2581358

関連資料:

その他の識別子:

連絡先:

更新時刻: 2026-02-27 12:30:07 +0900

MDRでの公開時刻: 2026-02-27 09:51:39 +0900