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

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.

Automated summary

The main challenge for 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. In this study, a fully automated routine combining convolutional neural network and machine vision was developed to mine data from transmission electron microscopy images of FePt-C nanogranular media. This allowed the generation of a dataset and implementation of a machine learning optimization model to guide process parameters, resulting in the desired nanostructure successfully validated experimentally. This work demonstrates the potential of data-driven design for high-density HAMR media.