論文 Predicting the surface roughness of an electrodeposited copper film using a machine learning technique

Ryo Tamura SAMURAI ORCID (Center for Basic Research on Materials/Data-driven Materials Research Field/Data-driven Algorithm Team, National Institute for Materials ScienceROR) ; Ryuichi Inaba (MITSUBISHI MATERIALS CORPORATION) ; Mami Watanabe (MITSUBISHI MATERIALS CORPORATION) ; Yutaro Mori (MITSUBISHI MATERIALS CORPORATION) ; Makoto Urushihara (MITSUBISHI MATERIALS CORPORATION) ; Kenji Yamaguchi (MITSUBISHI MATERIALS CORPORATION) ; Shoichi Matsuda SAMURAI ORCID (Research Center for Energy and Environmental Materials (GREEN)/Battery and Cell Materials Field/Automated Electrochemical Experiments Team, National Institute for Materials ScienceROR)

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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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