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This paper reports a machine learning approach for evaluating micromagnetic and microstructural parameters from demagnetization curves of FePt granular films for heat-assisted magnetic recording (HAMR) media. We developed a neural network to predict parameters of magnetic anisotropy and volume fractions of defects such as [200] misoriented grains, {111} twined variants and disordered grains. his work paves the way for high-throughput magnetometry-based characterization of FePt granular media for its structural optimization toward higher areal density of HAMR.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Magnetic recording, Micromagnetic simulations, Machine learning, FePt media

刊行年月日: 2022-05-13

出版者: Elsevier BV

掲載誌:

• Scripta Materialia (ISSN: 13596462) vol. 218 114797

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4848

公開URL: https://doi.org/10.1016/j.scriptamat.2022.114797

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更新時刻: 2024-10-15 16:30:13 +0900

MDRでの公開時刻: 2024-10-15 16:30:14 +0900