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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.

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• Creative Commons BY Attribution 4.0 International
  

Keyword: Machine learning, principal component analysis (PCA), reactive magnetron sputtering, plasma emission spectrum, Mössbauer spectroscopy

Date published: 2025-12-31

Publisher: Taylor & Francis

Journal:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 5 issue. 1 2544523

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1080/27660400.2025.2544523

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Updated at: 2025-09-10 16:30:38 +0900

Published on MDR: 2025-09-10 16:19:31 +0900