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36 results for “Magnon”
data for Tunable magnonic crystal in a hybrid superconductor–ferrimagnet nanostructure
<h3><strong>General description</strong></h3> <p>The data set designed for the reconstruction of the graphs identified in the manuscript as Fig. 2, Fig. 4 and Fig. 6 has been compiled. The data were respectively described as <strong>calculations</strong> relating to the method of obtaining the data (Brandt's method then plane wave method - PWM) and <strong>simulations</strong> (finite element method - FEM). </p> <h3><strong>Fig. 2</strong></h3> <p>In the case of Fig. 2(a-f), the data marked with a black solid line have been included. The data set includes information on the (x,y) components of the magnetic field induction generated by the superconductor (expressed in millitesla) in the x-direction (expressed in metres). </p> <h3><strong>Fig. 4</strong></h3> <p>The data set for each subsection of Fig. 4 comprises the results of the simulations and calculations. The resulting data from the simulations are classified according to their respective modes. For each mod, the wave vector (expressed as part of the first Brillouin zone) and frequency (GHz) are determined. The data sets resulting from the semi-analytical calculations comprise the common axis (1st column) of the wave vector (expressed as part of the Brillouin zone) followed by columns containing frequency for each mode (GHz).</p> <p> </p> <h3><strong>Fig. 6</strong></h3> <p>The data pertaining to the subsections of Fig. 6(a) and (b) are stored in a separate set of files. The data set for Fig. 6a comprises frequencies that indicate the boundaries of bands in relation to the external magnetic field (B_{0}). Each column has been assigned a number. The numbering is from the lowest frequencies to the highest for k_{x}=0. Each line is comprised of two columns, the first of which describes the magnetic field induction (expressed in millitesla) and the second of which describes the frequency (expressed in GHz). <br>Subsection (b) contains the dependence of the band boundaries (frequencies) on the separation between superconductors (d). The lines are also numbered from lowest frequency to highest for k_{x}=0. Each line is described by two columns, named by a number. The first column is the separation (expressed in nanometres), and the second is the frequency (expressed in GHz). </p>
Dataset for "Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers"
<p>The dataset consist of the data of the numerical simulations used to prepare the figures for the manuscript: </p><p>Krzysztof Szulc, Silvia Tacchi, Aurelio Hierro-Rodríguez, Javier Díaz, Paweł Gruszecki, Piotr Graczyk, Carlos Quirós, Daniel Markó, José Ignacio Martín, María Vélez, David S. Schmool, Giovanni Carlotti, Maciej Krawczyk, and Luis Manuel Álvarez-Prado. <i>Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers</i>. ACS Nano <strong>2022</strong> <i>16</i> (9), 14168-14177.</p><p>Please read README.txt file to see the description of the data in the files.</p>
Dataset of article entitled: "Tuning magnonic devices with on-chip permanent micromagnets"
<p>These are the dataset relative to paper entitled "Tuning magnonic devices with on-chip permanent micromagnets" published in <em>Physical Review Applied</em> </p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"
<p>Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures</p>
Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn2Au
<p>Data for the publication "<strong>Terahertz Néel spin-orbit torques drive nonlinear magnon dynamics in antiferromagnetic Mn<sub>2</sub>Au"</strong>, published in <em>Nat Commun</em> <strong>14</strong>, 6038 (2023). (https://doi.org/10.1038/s41467-023-41569-z).</p> <p>A preprint (2023) can be found on arxiv (https://doi.org/10.48550/arXiv.2305.03368).</p> <p>The datasets are provided for Figures 2-4.</p> <p>Files are provided as comma-separated text files with column headers. The value delimiter is comma " , ". The decimal separator is period " . "</p>
Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects
<p>The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".</p>
Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles
<p>Dataset of the publication: Magnon Straintronics in the 2D van der Waals Ferromagnet CrSBr from First-Principles</p> <p>DOI: 10.1021/acs.nanolett.2c02863</p> <p>D. L. Esteras, A. Rybakov, A. M. Ruiz, J. J. Baldoví</p> <p>Nano Lett. 2022, 22, 21, 8771–8778</p>
Data supplement for "Topological magnon band structure of emergent Landau levels in a skyrmion lattice"
<p>Collection of the data sets for our paper, <a href="https://doi.org/10.1126/science.abe4441"><em>Topological magnon band structure of emergent Landau levels in a skyrmion lattice</em></a>. (The source code supplement can be found <a href="https://doi.org/10.5281/zenodo.5718363">here</a>.)</p> <p> </p> <p><strong>Contents</strong></p> <table> <caption>Data files used for the paper's figures.</caption> <thead> <tr> <th scope="col">Scan</th> <th scope="col">Figure</th> <th scope="col">File(s)</th> </tr> </thead> <tbody> <tr> <td>(i)</td> <td>2</td> <td>ill_thales/exp_4-01-1621/rawdata/025280<br> ill_thales/exp_4-01-1621/rawdata/025281</td> </tr> <tr> <td>(ii)</td> <td>S17</td> <td>ill_thales/exp_INTER-436/rawdata/022169</td> </tr> <tr> <td>(iii)</td> <td>2</td> <td>ill_thales/exp_4-01-1597/rawdata/023454</td> </tr> <tr> <td>(iv)</td> <td>3</td> <td>mlz_reseda/*</td> </tr> <tr> <td>(v)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020778<br> ill_thales/exp_INTER-413/rawdata/020779</td> </tr> <tr> <td>(vi)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020777</td> </tr> <tr> <td>(vii)</td> <td>S16</td> <td>ill_thales/exp_INTER-436/rawdata/022168</td> </tr> <tr> <td>(viii)</td> <td>S16</td> <td>ill_thales/exp_INTER-413/rawdata/020793</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023488</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023489</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023453</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023553<br> ill_thales/exp_4-01-1597/rawdata/023559</td> </tr> <tr> <td> </td> <td>S12</td> <td>ill_thales/exp_INTER-436/rawdata/022213<br> ill_thales/exp_INTER-436/rawdata/022216<br> ill_thales/exp_INTER-436/rawdata/022217</td> </tr> </tbody> </table> <p> </p> <table> <caption>Overview of experimental data sets.</caption> <thead> <tr> <th scope="col">Instrument</th> <th scope="col">Proposal</th> <th scope="col">Directory</th> </tr> </thead> <tbody> <tr> <td><a href="http://doi.org/10.1080/10448632.2015.1057050">THALES (ILL)</a></td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-413">INTER-413</a></td> <td>ill_thales/exp_INTER-413/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-436">INTER-436</a></td> <td>ill_thales/exp_INTER-436/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1597">4-01-1597</a></td> <td>ill_thales/exp_4-01-1597/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-477">INTER-477</a></td> <td>ill_thales/exp_INTER-477/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1621">4-01-1621</a></td> <td>ill_thales/exp_4-01-1621/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2011.01.173">LET (RAL)</a></td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1620412">RB1620412</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1720033">RB1720033</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td><a href="https://www.psi.ch/en/sinq/tasp">TASP (PSI)</a></td> <td>20181324 (part 1)</td> <td>psi_tasp/exp_20181324_1/</td> </tr> <tr> <td> </td> <td>20181324 (part 2)</td> <td>psi_tasp/exp_20181324_2/</td> </tr> <tr> <td> </td> <td>20151888</td> <td>psi_tasp/exp_20151888/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2017.09.063">MIRA (MLZ)</a></td> <td>13511</td> <td>mlz_mira/exp_13511</td> </tr> <tr> <td> </td> <td>15633</td> <td>mlz_mira/exp_15633</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2019.05.056">RESEDA (MLZ)</a></td> <td>P00745-01</td> <td>mlz_reseda/</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank E. Villard and P. Chevalier for technical support and J. Locatelli for IT support during the <em>THALES</em> experiments; and J. Frank for technical support during the <em>MIRA</em> experiments. We thank J. K. Jochum for support with the <em>RESEDA</em> experiment. We thank M. Kugler for his early experiments on skyrmion dynamics in MnSi.</p> <p> </p> <p>► Please see the <strong>readme.txt</strong> file in the archive for details.</p> <p> </p>
Directional excitation of a high-density magnon gas using coherently driven spin waves
<p>Data corresponding to the figures of the main text of: "Directional excitation of a high-density magnon gas using coherently driven spin waves "</p>
Theoretical simulations data for "Experimental observation of repulsively bound magnons"
<p>The files contain the data obtained from the theoretical simulations depicted in the figures of the article "Experimental observation of repulsively bound magnons" Nature (2024).</p> <p>The format of the data and to which figure it corresponds is described in the file "read_me_metadata.txt".</p>
Dataset for the publication "Reversing the magnetization of 50-nm-wide ferromagnets by ultrashort magnons in thin-film Yttrium Iron Garnet"
<p>Dataset belonging to the manuscript "Reversing the magnetization of 50-nm-wide ferromagnets by ultrashort magnons in thin-film Yttrium Iron Garnet" published in Nanoscale Horizons, doi: <a href="https://doi.org/10.1039/D4NH00095A">https://doi.org/10.1039/D4NH00095A</a></p> <p>Every specific folder contains a text file that explains the measurement parameters and file formats.</p> <p>Abstract:</p> <p>Spin waves (magnons) can enable neuromorphic computing by which one aims at overcoming limitations inherent to conventional electronics and the von Neumann architecture. Encoding magnon signal by reversing magnetization of a nanomagnetic memory bit is pivotal to realize such novel computing schemes efficiently. A magnonic neural network was recently proposed consisting of differently configured nanomagnets that control nonlinear magnon interference in an underlying yttrium iron garnet (YIG) film [Papp et al., Nature communications, 2021, 12, 6422]. In this study, we explore the nonvolatile encoding of magnon signals by switching the magnetization of periodic and aperiodic arrays (gratings) of Ni81Fe19 (Py) nanostripes with widths w between 50 nm and 200 nm. Integrating 50-nm-wide nanostripes with a coplanar waveguide, we excited magnons having a wavelength λ of ≈100 nm. At a small spin-precessional power of 11 nW, these ultrashort magnons switch the magnetization of 50-nm-wide Py nanostripes after they have propagated over 25 μm in YIG. We also demonstrate the magnetization reversal of nanostripes patterned in an aperiodic sequence. We thereby show that the magnon-induced reversal happens regardless of the width and periodicity of the nanostripe gratings. Our study enlarges substantially the parameter regime for magnon-induced nanomagnet reversal on YIG and is important for realizing in-memory computing paradigms making use of magnons with ultrashort wavelengths at low power consumption."</p>
Data for the article "Evidence for spin current driven Bose-Einstein condensation of magnons"
<p>Data for the article "Evidence for spin current driven Bose-Einstein condensation of magnons"</p>
Terahertz field-driven magnon upconversion in an antiferromagnet
<p>This repository presents the raw data for the paper "Terahertz field-driven magnon upconversion in an antiferromagnet".</p><p>Data presented in the Supplementary Materials will be provided upon request. For such requests or general questions regarding the paper, please contact Zhuquan Zhang (zhuquan@mit.edu), Frank Y. Gao (frankgao@austin.utexas.edu), Edoardo Baldini (edoardo.baldini@austin.utexas.edu), or Keith Nelson (kanelson@mit.edu).</p>
Dataset for "Reconfigurable spin-wave platform based on interplay between nanodots and waveguide in hybrid magnonic crystal"
<p>The dataset consist of the data used to prepare the figures for the manuscript: </p> <p>Krzysztof Szulc, Mateusz Zelent, Maciej Krawczyk<br><em>Reconfigurable spin-wave platform based on interplay between nanodots and waveguide in hybrid magnonic crystal.</em></p> <p>together with animated version of two figures: Figure 4 and Figure S1.</p> <p>Please read README.txt file to see the description of the data in the files.</p>
Data for the article "Magnon transport in the presence of antisymmetric exchange in a weak antiferromagnet"
<p>Data for the article "Magnon transport in the presence of antisymmetric exchange in a weak antiferromagnet"</p> <p>(<a href="https://www.sciencedirect.com/science/article/abs/pii/S0304885321008714">https://www.sciencedirect.com/science/article/abs/pii/S0304885321008714</a> and <a href="https://arxiv.org/ftp/arxiv/papers/2106/2106.12853.pdf">https://arxiv.org/ftp/arxiv/papers/2106/2106.12853.pdf</a>)</p>
Data set for "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction"
<p>Scripts for the micromagnetic simulations of the publication "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction", using the OOMMF software. These codes reproduce the result of magnonic waveguides with periodic Dzyaloshinskii-Moriya interactions. A Dockerfile and a Makefile are included for the reproducibility of the results.</p> <p>The repository containing these results, together with a explanatory README document can be found in:</p> <p>https://github.com/davidcortesortuno/paper-2018-chiral_magnonic_crystals</p> <p> </p> <p>The files included in this Zenodo release refer to the v1.0 version of the data set. For an updated version of the scripts refer to the Github repository.</p>
Stimulated magnon scattering by non-degenerate parametric excitation: Figure data
<p>This repository contains the data displayed in the five figures of the paper:</p> <p><strong>Stimulated magnon scattering by non-degenerate parametric excitation</strong><br><em>Joo-Von Kim, Hugo Merbouche</em><br><a href="https://doi.org/10.1063/5.0223157">doi:10.1063/5.0223157</a></p> <p>We used the scientific color maps developed by Crameri <em>et al</em>. (<a href="https://doi.org/10.5281/zenodo.8409685">doi:10.5281/zenodo.8409685</a>, <a href="https://doi.org/10.1038/s41467-020-19160-7">doi:10.1038/s41467-020-19160-7</a>), namely <code>batlowW</code>, <code>oslo</code>, and <code>vik</code>, in order to minimize visual distortion of the data and artifacts for readers with color-vision deficiencies.</p> <p>Data were visualized using <a href="https://veusz.github.io">Veusz</a> and <a href="https://www.wolfram.com/mathematica/">Wolfram Mathematica</a> (v14.0), and assembled with <a href="https://affinity.serif.com/designer/">Affinity Designer</a> (v2.0).</p> <p>Mode profiles [as presented in Fig. 2(a) of the paper] are provided in the <a href="https://math.nist.gov/oommf/doc/userguide12b4/userguide/OVF_2.0_format.html">OOMMF OVF 2.0</a> format.</p>
Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions "
<p>Data for the article "Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions" </p> <p>URL: https://arxiv.org/abs/2205.11965<br> DOI: In press in Nature Communications</p>
Magnon-mediated qubit coupling determined via dissipation measurements
<p>Accompanying data for the main text and supporting information of <i><strong>Magnon-mediated qubit coupling determined via dissipation measurements</strong></i></p><p>Paper abstract is as follows:</p><p>Controlled interaction between localized and delocalized solid-state spin systems offers a compelling platform for on-chip quantum information processing with quantum spintronics. Hybrid quantum systems (HQSs) of localized nitrogen-vacancy (NV) centers in diamond and delocalized magnon modes in ferrimagnets–systems with naturally commensurate energies–have recently attracted significant attention, especially for interconnecting isolated spin qubits at length-scales far beyond those set by the dipolar coupling. However, despite extensive theoretical efforts, there is a lack of experimental characterization of the magnon-mediated interaction between NV centers, which is necessary to develop such hybrid quantum architectures. Here, we experimentally determine the magnon-mediated NV-NV coupling from the magnon-induced self-energy of NV centers. Our results are quantitatively consistent with a model in which the NV center is coupled to magnons by dipolar interactions. This work provides a versatile tool to characterize HQSs in the absence of strong coupling, informing future efforts to engineer entangled solid-state systems.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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