Machine learning-driven analysis and process optimization of FePt-BN granular films for HAMR applications.

The ever-increasing demand for high-density, energy-efficient data storage, propelled by AI and cloud infrastructures, is driving advancements in heat-assisted magnetic recording (HAMR) media. L10-ordered FePt granular thin films are recognized as leading candidates owing to their exceptional thermal stability and potential for sub-10 nm grain sizes. Here, we investigate FePt-BN granular films, systematically optimizing their microstructure and magnetic properties by tuning process parameters including BN atomic fraction of FePt-BN targets, N2 gas flow, and film thickness. Employing a hybrid workflow that integrates conventional trial-and-error experimentation with machine learning such as principle component analysis, random forest regression and Bayesian optimization, we identify key determinants governing average main- and sub-grain size (D 1, D 2), coercivity (μ 0 H c), and chemical ordering. Machine-learning analysis reveals that the degree of L10 order is the primary factor for μ 0 H c, while D 1 is mainly influenced by BN content and microstructural attributes derived from transmission electron microscopy data. Active-learning-guided Bayesian optimization enabled us to rapidly achieve FePt-BN media with high μ 0 H c (2.4 T), sub 6 nm grain diameters, and areal grain densities exceeding 12 Tgrains/in2 outperforming traditional heuristic approaches with fewer experiments. Our results underscore the power of data-driven process optimization for accelerated HAMR materials development, enabling the realization of next-generation hard disk drives with ultra-high recording densities.