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• W4378549672 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." @default.
• W4378549672 created "2023-05-28" @default.
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• W4378549672 date "2023-08-01" @default.
• W4378549672 modified "2023-09-26" @default.
• W4378549672 title "Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation" @default.
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• W4378549672 doi "https://doi.org/10.1016/j.actamat.2023.119039" @default.
• W4378549672 hasPublicationYear "2023" @default.
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