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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.

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• Creative Commons BY Attribution 4.0 International
  

Keyword: Magnetic recording, Micromagnetic simulations, Machine learning, FePt media

Date published: 2022-05-13

Publisher: Elsevier BV

Journal:

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

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1016/j.scriptamat.2022.114797

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Updated at: 2024-10-15 16:30:13 +0900

Published on MDR: 2024-10-15 16:30:14 +0900