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Reactive sputtering is a complex process in which the valence state of the deposited material and the deposition rate are highly sensitive to growth conditions. Reliable monitoring is essential for achieving reproducible and high-quality thin film growth; however, practical methods remain limited. In this study, we developed a real-time analysis method that combines broad-range plasma emission spectroscopy with principal component analysis (PCA). The results demonstrate that the valence state and deposition rate of iron oxide thin films can be accurately predicted using the first and second principal components. This approach offers a promising tool for real-time prediction and control of the deposition process.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Machine learning, principal component analysis (PCA), reactive magnetron sputtering, plasma emission spectrum, Mössbauer spectroscopy

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 2544523

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5750

公開URL: https://doi.org/10.1080/27660400.2025.2544523

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更新時刻: 2025-09-10 16:30:38 +0900

MDRでの公開時刻: 2025-09-10 16:19:31 +0900