Optimizing Time-resolved Magneto-optical Kerr Effect for High-fidelity Magnetic Characterization

TL;DR

This paper develops experimentally validated guidelines to optimize Time-resolved Magneto-Optical Kerr Effect (TR-MOKE) measurements, enhancing accuracy and sensitivity for magnetic property characterization in spintronic materials.

Contribution

It introduces a systematic optimization framework for TR-MOKE, improving measurement sensitivity and reliability for materials with different magnetic anisotropies.

Findings

Optimal field angles identified for high signal amplitude

Enhanced sensitivity to $H_{k,eff}$ and damping $\\alpha$

Suppression of inhomogeneity effects

Abstract

Spintronics has emerged as a key technology for fast and non-volatile memory with great CMOS compatibility. As the building blocks for these cutting-edge devices, magnetic materials require precise characterization of their critical properties, such as the effective anisotropy field ($H_{\rm{k,eff}}$, related to magnetic stability) and damping ($\alpha$ key factor in device energy efficiency). Accurate measurements of these properties are essential for designing and fabricating high-performance spintronic devices. Among advanced metrology techniques, Time-resolved Magneto-Optical Kerr Effect (TR-MOKE) stands out for its superb temporal and spatial resolutions, surpassing traditional methods like ferromagnetic resonance (FMR). However, the full potential of TR-MOKE has not yet been fully pledged due to the lack of systematic optimization and robust operational guidelines. In this study, we address this gap by developing experimentally validated guidelines for optimizing TR-MOKE metrology across materials with perpendicular magnetic anisotropy (PMA) and in-plane magnetic anisotropy (IMA). Our work identifies the optimal ranges of the field angle to simultaneously achieve high signal amplitudes and improve measurement sensitivities to $H_{\rm{k,eff}}$ and $\alpha$. By suppressing the influence of inhomogeneities and boosting sensitivity, our work significantly enhances TR-MOKE capability to extract magnetic properties with high accuracy and reliability. This optimization framework positions TR-MOKE as an indispensable tool for advancing spintronics, paving the way for energy-efficient and high-speed devices that will redefine the landscape of modern computing and memory technologies.