Direct observation of Σ7 domain boundary core structure in magnetic skyrmion lattice

Matsumoto, Takao; So, Yeong-Gi; Kohno, Yuji; Sawada, Hidetaka; Ikuhara, Yuichi; Shibata, Naoya

doi:10.1126/sciadv.1501280

Cited by 105 publications

(102 citation statements)

References 26 publications

(29 reference statements)

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“…As a result, the skyrmions in the defect zone be expected to become elliptically distorted, as observed using Lorentz transmission electron microscopy (LTEM) on thinned-down FeGe 1-x Si x bulk samples. 38 Moreover, the close-packed ordering of the skyrmion lattice can have defects at the domain boundaries, e.g., "5-7 defects" where five or seven skyrmions are surrounding a reference skyrmion, instead of six. 37 In this case, a line defect may appear, forming the domain boundary.…”

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confidence: 99%

Imaging and manipulation of skyrmion lattice domains in Cu2OSeO3

Zhang

Bauer

Berger

et al. 2016

Applied Physics Letters

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Nanoscale chiral skyrmions in noncentrosymmetric helimagnets are promising binary state variables in high-density, low-energy nonvolatile memory. Nevertheless, they normally appear in an ordered, single-domain lattice phase, which makes it difficult to write information unless they are spatially broken up into smaller units, each representing a bit. Thus, the formation and manipulation of skyrmion lattice domains is a prerequisite for memory applications. Here, using an imaging technique based on resonant magnetic x-ray diffraction, we demonstrate the mapping and manipulation of skyrmion lattice domains in Cu2OSeO3. The material is particularly interesting for applications owing to its insulating nature, allowing for electric field-driven domain manipulation.

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confidence: 99%

Imaging and manipulation of skyrmion lattice domains in Cu2OSeO3

Zhang

Bauer

Berger

et al. 2016

Applied Physics Letters

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“…For instance, skyrmion formation mediated by grain boundaries has been observed in chiral magnets [6,32,33]. Moreover, the disorder induced by the structural domain boundaries could possibly lead to a spatial variation of the cycloidal wavelength and the SkL lattice constant.…”

Section: Introductionmentioning

confidence: 99%

Relaxation dynamics of modulated magnetic phases in the skyrmion host GaV4S8 : An ac magnetic susceptibility study

et al. 2017

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We report on the slow magnetization dynamics observed upon the magnetic phase transitions of GaV 4 S 8 , a multiferroic compound featuring a long-ranged cycloidal magnetic order and a Néel-type skyrmion lattice in a relatively broad temperature range below its Curie temperature. The fundamental difference between GaV 4 S 8 and the chiral helimagnets, the prototypical skyrmion host compounds, lies within the polar symmetry of GaV 4 S 8 , promoting a cycloidal instead of a helical magnetic order and rendering the magnetic phase diagram essentially different from that in the cubic helimagnets. Our ac magnetic susceptibility study reveals slow relaxation dynamics at the field-driven phase transitions between the cycloidal, skyrmion lattice and field-polarized states. At each phase boundary, the characteristic relaxation times were found to exhibit a strong temperature dependence, starting from the minute range at low temperatures, decreasing to the micro-to millisecond range at higher temperatures.

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“…Furthermore, because our pattern recognition analysis is general in nature, it can be extended to microscopic measurements of structural, electronic, and magnetic orders, as well as their possible spatial correlations, in a variety of condensed matter systems such as, for example, skyrmion lattices. 40 Data availability All the relevant data is available from the authors upon request. Fig.…”

Section: Discussionmentioning

confidence: 99%

Learning surface molecular structures via machine vision

2017

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Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify ('read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function-a centerpiece in analysis of disorderproperty relationship paradigm-as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

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Direct observation of Σ7 domain boundary core structure in magnetic skyrmion lattice

Abstract: Advanced electron microscopy reveals a unique structural relaxation mechanism in a magnetic skyrmion domain boundary core.

Cited by 105 publications

References 26 publications

Imaging and manipulation of skyrmion lattice domains in Cu2OSeO3

Imaging and manipulation of skyrmion lattice domains in Cu2OSeO3

Relaxation dynamics of modulated magnetic phases in the skyrmion host GaV4S8 : An ac magnetic susceptibility study

Learning surface molecular structures via machine vision

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