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 (D1, D2), coercivity (μ0Hc), and chemical ordering. Machine-learning analysis reveals that the degree of L10 order is the primary factor for μ0Hc, while D1 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 μ0Hc (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.