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arXiv 2103.14449cond-mat.mtrl-scicond-mat.str-el
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通过非线性磁响应探测拓扑自旋织构

Detection of Topological Spin Textures via Non-Linear Magnetic Responses

Mariia Stepanova, Jan Masell, Erik Lysne, Peggy Schoenherr, Laura Köhler, Alireza Qaiumzadeh, Naoya Kanazawa, Achim Rosch, Yoshinori Tokura, Arne Brataas, Marku… 展开作者

Mariia Stepanova, Jan Masell, Erik Lysne, Peggy Schoenherr, Laura Köhler, Alireza Qaiumzadeh, Naoya Kanazawa, Achim Rosch, Yoshinori Tokura, Arne Brataas, Markus Garst, Dennis Meier

AI总结:

针对拓扑非平庸自旋织构高分辨率高灵敏度探测难题,研究FeGe中多种自旋织构的局域磁性质,结合磁力显微镜与微磁模拟揭示其非线性磁响应特征,提出基于超导微线圈的创新读出方案。

摘要:

拓扑非平庸自旋织构(如斯格明子和位错)展现出涌现的电动力学特性,可被自旋电流推动跨越宏观距离。这些独特性质及其纳米尺度尺寸,使其成为开发下一代逻辑门、赛道存储器以及神经形态计算人工突触的理想候选材料。此类应用(以及广义上纳米级磁结构研究)面临的一大主要挑战,是实现兼具高分辨率与高灵敏度的探测方案。我们研究了FeGe中向错、位错和畴壁的局域磁性质,揭示出一种显著的响应,可将单个自旋织构与螺旋磁背景区分开来。通过磁力显微镜与微磁模拟相结合,证实这种非线性响应与局域磁化率相关。基于上述发现,我们提出了一种采用超导微线圈的读出方案,为在器件相关几何结构中探测拓扑非平庸自旋织构和畴壁提供了一种创新方法。

英文摘要:

Topologically non-trivial spin textures, such as skyrmions and dislocations, display emergent electrodynamics and can be moved by spin currents over macroscopic distances. These unique properties and their nanoscale size make them excellent candidates for the development of next-generation logic gates, race-track memory, and artificial synapses for neuromorphic computing. A major challenge for these applications - and the investigation of nanoscale magnetic structures in general - is the realization of detection schemes that provide high resolution and sensitivity. We study the local magnetic properties of disclinations, dislocations, and domain walls in FeGe, and reveal a pronounced response that distinguishes the individual spin textures from the helimagnetic background. Combination of magnetic force microscopy and micromagnetic simulations links the non-linear response to the local magnetic susceptibility. Based on the findings, we propose a read-out scheme using superconducting micro-coils, representing an innovative approach for detecting topologically non-trivial spin textures and domain walls in device-relevant geometries.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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