ジャーナル論文 Predicting the surface roughness of an electrodeposited copper film using a machine learning technique
Ryo Tamura (author) (この著者で検索)
ORCID https://orcid.org/0000-0002-0349-358X
National Institute for Materials Science Center for Basic Research on Materials/Data-driven Materials Research Field/Data-driven Algorithm Team
SAMURAI NIMS Researchers Directory SAMURAI
ORCID SAMURAI ;
Ryuichi Inaba (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Mami Watanabe (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Yutaro Mori (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Makoto Urushihara (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Kenji Yamaguchi (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Shoichi Matsuda (author) (この著者で検索)
ORCID https://orcid.org/0000-0002-0640-3404
National Institute for Materials Science Research Center for Energy and Environmental Materials (GREEN)/Battery and Cell Materials Field/Automated Electrochemical Experiments Team
SAMURAI NIMS Researchers Directory SAMURAI
ORCID SAMURAI
コレクション

引用
Ryo Tamura, Ryuichi Inaba, Mami Watanabe, Yutaro Mori, Makoto Urushihara, Kenji Yamaguchi, Shoichi Matsuda. Predicting the surface roughness of an electrodeposited copper film using a machine learning technique. Science and Technology of Advanced Materials: Methods. 2024, 4 (1), 2416889. https://doi.org/10.1080/27660400.2024.2416889
SAMURAI

説明:

(abstract)

Electrodeposition-based metal coating techniques are used to manufacture various industrial products and rely on the quantitative control of the physical properties of the coating layers, such as electrical conductivity, surface roughness, and hardness. To clarify the experimental conditions required to realize the desired physical properties of metal coating layers and shed light on the complex mechanism of the involved reactions, we prepared a custom-built experimental dataset (60 conditions) on the surface roughness of electrodeposited thin copper films and submitted it to an open-access data repository. Data-driven analysis revealed that surface roughness is strongly affected by the deposition temperature, current, and interelectrode distance.

権利情報:

キーワード: electrodeposited copper film, surface roughness, machine learning

刊行年月日: 2024-12-31

出版者: Taylor & Francis

掲載誌:

  • Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 4 issue. 1 2416889

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2024.2416889

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更新時刻: 2024-10-31 16:30:15 +0900

MDRでの公開時刻: 2024-10-31 16:30:16 +0900

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