Download file (1.64MB)
Download as zip (1.58MB)

コレクション

引用

説明:

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.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: Heat-assisted magnetic recording (HAMR), FePt, Deep learning, Machine learning, Image segmentation

刊行年月日: 2023-05-27

出版者: Elsevier BV

掲載誌:

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

研究助成金:

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

原稿種別: 著者最終稿 (Accepted manuscript)

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

公開URL: https://doi.org/10.1016/j.actamat.2023.119039

関連資料:

その他の識別子:

連絡先:

更新時刻: 2025-05-27 08:30:20 +0900

MDRでの公開時刻: 2025-05-27 08:21:21 +0900