Article Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation

N. Kulesh SAMURAI ORCID (National Institute for Materials ScienceROR) ; A. Bolyachkin SAMURAI ORCID (National Institute for Materials ScienceROR) ; I. Suzuki ORCID (National Institute for Materials Science) ; Y.K. Takahashi SAMURAI ORCID (National Institute for Materials ScienceROR) ; H. Sepehri-Amin SAMURAI ORCID (National Institute for Materials ScienceROR) ; K. Hono SAMURAI ORCID (National Institute for Materials ScienceROR)

Manuscript.pdf
Download file (1.64 MB)
Download as zip (1.58 MB)
Collection

Citation
N. Kulesh, A. Bolyachkin, I. Suzuki, Y.K. Takahashi, H. Sepehri-Amin, K. Hono. Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation. Acta Materialia. 2023, 255 (), 119039. https://doi.org/10.1016/j.actamat.2023.119039

Description:

(abstract)

The main bottleneck for heat-assisted magnetic recording (HAMR) to achieve a potential areal density of 4 Tb/in2 is the difficulty in obtaining FePt-X nanogranular media with an ideal stacking structure of perfectly isolated L10-FePt columnar nanograins. Here, we present a fully automated routine that combines a convolutional neural network and machine vision to enable data mining from transmission electron microscopy images of FePt-C nanogranular media. This allowed us to generate a dataset and implement a machine learning optimization model that guides process parameters to achieve the desired nanostructure, i.e., small grain size with unimodal distribution and a large coercivity, which was successfully validated experimentally. This work demonstrates the promise of data-driven design of high-density HAMR media.

Rights:

Keyword: Heat-assisted magnetic recording (HAMR), FePt, Deep learning, Machine learning, Image segmentation

Date published: 2023-05-27

Publisher: Elsevier BV

Journal:

  • Acta Materialia (ISSN: 13596454) vol. 255 119039

Funding:

  • Japan Science and Technology Agency
  • Government of Japan Ministry of Education Culture Sports Science and Technology

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.4883

First published URL: https://doi.org/10.1016/j.actamat.2023.119039

Related item:

Other identifier(s):

Contact agent:

Updated at: 2025-05-27 08:30:20 +0900

Published on MDR: 2025-05-27 08:21:21 +0900

Filename Size
Filename Manuscript.pdf (Thumbnail)
application/pdf 		Size 1.64 MB Detail