Super-hierarchical and explanatory analysis of magnetization reversal process using topological data analysis

Abstract

The microstructures of magnetic domains are crucial in determining the functions of spintronic devices. However, the magnetization reversal mechanism is still not fully understood because of the difficulty in quantifying the drastic and complex changes in the magnetic domain structure. Here, we used topological data analysis and developed a super-hierarchical and explanatory analysis method for magnetic reversal processes. We quantified the complexity of a magnetic domain structure using persistent homology and visualized the magnetization reversal process in a two-dimensional space using principal component analysis. The first principal component (PC1) was a descriptor explaining the magnetization, and the second principal component (PC2) was a crucial descriptor characterizing the stability of the magnetic domain structure. Interestingly, PC2 detected slight changes in the structure, which indicates a hidden feature dominating the metastable/stable reversal processes. We successfully determined the cause of the branching of the macroscopic reversal process on the original microscopic magnetic domain structure. This super-hierarchical and explanatory analysis would improve the reliability of spintronics devices and understanding of stochastic/deterministic magnetization reversal phenomena.

Keywords

• super-hierarchical
• explanatory analysis
• magnetic domains
• magnetic reversal process
• persistent homology
• principal component analysis