Optimizing time-resolved magneto-optical Kerr effect for high-fidelity magnetic characterization
Abstract
Spintronics has emerged as a key technology for fast and nonvolatile 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 (Hk,eff, related to magnetic stability) and damping (α, a 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. However, the full potential of TR-MOKE has not yet been fully fledged 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 and in-plane magnetic anisotropy. While Co20Fe60B20 thin films are used for experimental validation, this optimization framework can be readily extended to a variety of materials such as L10-FePd with easy-axis dispersion. Our work identifies the optimal ranges of the field angle to simultaneously achieve high signal amplitudes and improve measurement sensitivities to Hk,eff and α. 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.
Article Details
Journal Info
Applied Physics Letters
American Institute of Physics
Authors (7)
Yun Kim
Dingbin Huang
Material Sciences Division
Deyuan Lyu
Department of Electrical and Computer Engineering, University of Minnesota 1 , Twin Cities, Minneapolis, Minnesota 55455,
HaoYue Sun
Jian-Ping Wang
Department of Electrical and Computer Engineering, University of Minnesota 1 , Twin Cities, Minneapolis, Minnesota 55455,
Paul A. Crowell
School of Physics and Astronomy, University of Minnesota 3 , Minneapolis, Minnesota 55455,
Xiaojia Wang
Department of Mechanical Engineering