{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FMR standard problem\n", "\n", "## Problem specification\n", "\n", "We choose a cuboidal thin film permalloy sample measuring $120 \\times 120 \\times 10 \\,\\text{nm}^{3}$. The choice of a cuboid is important as it ensures that the finite difference method employed by OOMMF does not introduce errors due to irregular boundaries that cannot be discretized well. We choose the thin film geometry to be thin enough so that the variation of magnetization dynamics along the out-of-film direction can be neglected. Material parameters based on permalloy are:\n", "\n", "- exchange energy constant $A = 1.3 \\times 10^{-11} \\,\\text{J/m}$,\n", "- magnetisation saturation $M_\\text{s} = 8 \\times 10^{5} \\,\\text{A/m}$,\n", "- Gilbert damping $\\alpha = 0.008$.\n", "\n", "An external magnetic bias field with magnitude $80 \\,\\text{kA/m}$ is applied along the direction $e = (1, 0.715, 0)$. We choose the external magnetic field direction slightly off the sample diagonal in order to break the system’s symmetry and thus avoid degenerate eigenmodes. First, we initialize the system with a uniform out-of-plane magnetization $m_{0} = (0, 0, 1)$. The system is allowed to relax for $5 \\,\\text{ns}$, which was found to be sufficient time to obtain a well-converged equilibrium magnetization configuration. We refer to this stage of simulation as the relaxation stage, and its final relaxed magnetization configuration is saved to serve as the initial configuration for the next dynamic stage. Because we want to use a well defined method that is supported by all simulation tools, we minimize the system’s energy by integrating the LLG equation with a large, quasistatic Gilbert damping $\\alpha = 1$ for $5 \\,\\text{ns}$. In the next step (dynamic stage), a simulation is started using the equilibrium magnetisation configuration from the relaxation stage as the initial configuration. Now, the direction of an external magnetic field is altered to $e = (1, 0.7, 0)$. This simulation stage runs for $T = 20 \\,\\text{ns}$ while the (average and spatially resolved) magnetization $M(t)$ is recorded every $\\Delta t = 5 \\,\\text{ps}$. The Gilbert damping in this dynamic simulation stage is $\\alpha = 0.008$.\n", "\n", "Details of this standard problem specification can be found in Ref. 1.\n", "\n", "## Relaxation stage\n", "\n", "Firstly, all required modules are imported." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "import oommfc as oc\n", "import discretisedfield as df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we specify all simulation parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "\n", "lx = ly = 120e-9 # x and y dimensions of the sample(m)\n", "lz = 10e-9 # sample thickness (m)\n", "dx = dy = dz = 5e-9 # discretisation in x, y, and z directions (m)\n", "\n", "Ms = 8e5 # saturation magnetisation (A/m)\n", "A = 1.3e-11 # exchange energy constant (J/m)\n", "H = 8e4 * np.array([0.81345856316858023, 0.58162287266553481, 0.0])\n", "alpha = 0.008 # Gilbert damping\n", "gamma = 2.211e5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the system object can be created and mesh, magnetisation, hamiltonian, and dynamics are specified." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mesh = oc.Mesh(p1=(0, 0, 0), p2=(lx, ly, lz), cell=(dx, dy, dz))\n", "\n", "system = oc.System(name=\"stdprobfmr\")\n", "\n", "system.hamiltonian = oc.Exchange(A) + oc.Demag() + oc.Zeeman(H)\n", "system.dynamics = oc.Precession(gamma) + oc.Damping(alpha)\n", "system.m = df.Field(mesh, value=(0, 0, 1), norm=Ms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the system is relaxed." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/9/25 14:50: Calling OOMMF (stdprobfmr/stdprobfmr.mif) ... [0.5s]\n" ] } ], "source": [ "md = oc.MinDriver()\n", "md.drive(system)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now load the relaxed state to the Field object and plot the $z$ slice of magnetisation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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+gWXAt0UaVwD+IU+XU0Lr2naiPvMUMPG2PuLt/aYcPRIlcczcdCx1nUSXOllt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\nz+VdsozSSy8x5GQXVbxvv0PJ3PMpmjQ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G4EOBnpjX86WUvuaqlcchqGFfNFtKOtjw51LT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gN8gQjm1UfQze88R3PhA+ex6x0JnkjK1p9D2nwRTMjIcC9/2EVjU/hhp7Ak2bDeuSTpnKbBYIgz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\nBf8PyEKsf6ETKn0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "system.m.plot_plane(\"z\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamic stage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the dynamic stage, we use the relaxed state from the relaxation stage." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Change external magnetic field.\n", "H = 8e4 * np.array([0.81923192051904048, 0.57346234436332832, 0.0])\n", "system.hamiltonian.zeeman.H = H" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we run the multiple stage simulation using `TimeDriver`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/9/25 14:50: Calling OOMMF (stdprobfmr/stdprobfmr.mif) ... [64.5s]\n" ] } ], "source": [ "T = 20e-9\n", "n = 4000\n", "\n", "td = oc.TimeDriver()\n", "td.drive(system, t=T, n=n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Postprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the obtained vector field samples, we can compute the average of magnetisation $y$ component and plot its time evolution. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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PNXh8fSYI2HUwCDC2hufFcxMeDRcyig9f3PnMVgAeDhc+ArjpgbV09Ti+cffz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\nQD0BIiIyOCkIKFF6uWDPby6TGDiQNRI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "t = system.dt['t'].as_matrix()\n", "my = system.dt['mx'].as_matrix()\n", "\n", "# Plot time evolution.\n", "plt.figure(figsize=(8, 6))\n", "plt.plot(t, my)\n", "plt.xlabel('t (ns)')\n", "plt.ylabel('my average')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the $$ time evolution, we can compute and plot its Fourier transform." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.fftpack\n", "\n", "psd = np.log10(np.abs(scipy.fftpack.fft(my))**2)\n", "f_axis = scipy.fftpack.fftfreq(4000, d=20e-9/4000)\n", "\n", "plt.plot(f_axis/1e9, psd)\n", "plt.xlim([6, 12])\n", "plt.xlabel('f (GHz)')\n", "plt.ylabel('Psa (a.u.)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "[1] A. Baker, M. Beg, G. Ashton, M. Albert, D. Chernyshenko, W. Wang, S. Zhang, M.-A. Bisotti, M. Franchin, C.L. Hu, R. Stamps, T. Hesjedal, and H. Fangohr, *J. Magn. Magn. Mater.* **421**, 428 (2017)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }