{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "P9-FPzw4D3-B", "tags": [] }, "source": [ "# Guided mode expansion of a PhC slab with a horizontal (xy) symmetry plane\n", "\n", "In this example we apply `legume` to calculate the bands of a PhC slab with a horizontal (xy) mirror plane, and we demonstrate how the bands can be separated according to reflection symmetry $\\sigma_{xy}$. This example is related to Sec. 2.3 of the CPC paper.\n", "\n", "The structure parameters are those of Ch. 8, Fig. 2(b) of [Molding the Flow of Light](http://ab-initio.mit.edu/book/) , and we focus on how to achieve separation with respect to $\\sigma_{xy}$ by choosing the indices of the guided modes in the expansion." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2475, "status": "ok", "timestamp": 1708441315632, "user": { "displayName": "SIMONE ZANOTTI", "userId": "14051006727806237496" }, "user_tz": -60 }, "id": "21sd2KrpVpKj", "outputId": "ad297911-ce8e-46b2-e6de-75be60e329d1", "tags": [] }, "outputs": [], "source": [ "import legume\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define an xy-symmetric photonic crystal slab" ] }, { "cell_type": "markdown", "metadata": { "id": "EyFLX0IoEvMX" }, "source": [ "Here we consider an xy-symmetric PhC slab, i.e., a slab that is symmetric with respect to mirror reflection in the xy plane. We separate the two polarizations as (see Sec. 2.3, Table 1 of the CPC paper):\n", "\n", "* $xy$-even or $\\sigma_{xy}=+1$ (or TE-like): we must use guided modes $\\alpha=0, 3, 4, 7, ...$ in the basis\n", "* $xy$-odd or $\\sigma_{xy}=-1$ (or TM-like): we must use guided modes $\\alpha=1, 2, 5, 6, ...$ in the basis\n", "\n", "In PhC literature, $xy$-even modes are often called TE-like, while $xy$-odd modes are often called TM-like, where the terminology is borrewed from the 2D case. We prefer to avoid this terminology when using `legume`, because we reserve the words `TE` or `TM` for the polarizations of the guided modes of the effective waveguide, which are defined with respect to a vertical mirror plane." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 264, "status": "ok", "timestamp": 1708441325002, "user": { "displayName": "SIMONE ZANOTTI", "userId": "14051006727806237496" }, "user_tz": -60 }, "id": "Gztnn_Iy1LaP", "outputId": "3463311e-31eb-409b-d07d-033f4a64a28e", "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reciprocal lattice vectors in the expansion: npw = 61\n" ] } ], "source": [ "D = 0.6 # slab thickness in units of a\n", "r = 0.3 # hole radius in units of a\n", "eps_c = 1.0 # permittivity of circular hole\n", "eps_b = 12.0 # background permittivity of slab\n", "eps_lower, eps_upper = 1, 1 # permittivities of lower and upper claddings\n", "\n", "lattice = legume.Lattice('hexagonal')\n", "phc = legume.PhotCryst(lattice, eps_l=eps_lower, eps_u=eps_upper)\n", "phc.add_layer(d=D, eps_b=eps_b)\n", "phc.layers[-1].add_shape(legume.Circle(eps=eps_c, r=r, x_cent=0., y_cent=0))\n", "\n", "gme = legume.GuidedModeExp(phc, gmax=5, truncate_g='abs')\n", "npw = np.shape(gme.gvec)[1] # number of plane waves in the expansion\n", "print(f'Number of reciprocal lattice vectors in the expansion: npw = {npw}')" ] }, { "cell_type": "markdown", "metadata": { "id": "HB70ZKPqCnVv" }, "source": [ "## Separately calculate xy-even and xy-odd modes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we run the guided-mode expansion for the two separate symmetries and store the bands." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 18818, "status": "ok", "timestamp": 1708441438359, "user": { "displayName": "SIMONE ZANOTTI", "userId": "14051006727806237496" }, "user_tz": -60 }, "id": "dV8zwnKmtmO-", "outputId": "050a18cc-b1df-4bab-c3b1-7a8fdd30147b", "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running gme k-points: │------------------------------│ 1 of 81" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running gme k-points: │██████████████████████████████│ 81 of 81\r" ] }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 61 plane waves and 2 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute='exact' │ 2.398 │ │████████████████----│ 82% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.002 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the 'eigh' solver │ 0.086 │ │--------------------│ 3% │\n",
"│ Creating GME matrix │ 0.393 │ │██------------------│ 13% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 81 k-points │ 2.914 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
"\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 61 plane waves and 2 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m2.398 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│████████████████----│ 82%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.002 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.086 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 3%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.393 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██------------------│ 13%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 81 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m2.914\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
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"Skipping imaginary part computation, use run_im() to run it, or compute_rad() to compute the radiative rates of \n", "selected eigenmodes\n", "\n" ], "text/plain": [ "Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n", "selected eigenmodes\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Number of wavevectors = 81, number of frequencies = 20\n", "\n", "Running gme k-points: │██████████████████████████████│ 81 of 81\r" ] }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Steps in GuidedModeExp: 61 plane waves and 2 guided modes ┃ Time (s) ┃ % vs total T ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ Guided modes computation with gmode_compute='exact' │ 2.363 │ │████████████████----│ 82% │\n",
"│ Inverse matrix of Fourier-space permittivity │ 0.000 │ │--------------------│ 0% │\n",
"│ Matrix diagionalization using the 'eigh' solver │ 0.086 │ │--------------------│ 3% │\n",
"│ Creating GME matrix │ 0.395 │ │██------------------│ 14% │\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│ Total time for real part of frequencies for 81 k-points │ 2.890 │ │████████████████████│ 100% │\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n",
"\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mSteps in GuidedModeExp: 61 plane waves and 2 guided modes\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTime (s)\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m % vs total T\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36mGuided modes computation with gmode_compute='\u001b[0m\u001b[1;36mexact\u001b[0m\u001b[36m'\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m2.363 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│████████████████----│ 82%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mInverse matrix of Fourier-space permittivity\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.000 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 0%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mMatrix diagionalization using the '\u001b[0m\u001b[1;36meigh\u001b[0m\u001b[36m' solver\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.086 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│--------------------│ 3%\u001b[0m\u001b[32m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36mCreating GME matrix\u001b[0m\u001b[36m \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m0.395 \u001b[0m\u001b[35m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m│██------------------│ 14%\u001b[0m\u001b[32m \u001b[0m│\n",
"├───────────────────────────────────────────────────────────┼──────────┼──────────────────────────────┤\n",
"│\u001b[1;36m \u001b[0m\u001b[1;36mTotal time for real part of frequencies for 81 k-points\u001b[0m\u001b[1;36m \u001b[0m\u001b[1;36m \u001b[0m│\u001b[1;35m \u001b[0m\u001b[1;4;35m2.890\u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m│\u001b[1;32m \u001b[0m\u001b[1;32m│████████████████████│ 100%\u001b[0m\u001b[1;32m \u001b[0m│\n",
"└───────────────────────────────────────────────────────────┴──────────┴──────────────────────────────┘\n"
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"text/html": [
"Skipping imaginary part computation, use run_im() to run it, or compute_rad() to compute the radiative rates of \n", "selected eigenmodes\n", "\n" ], "text/plain": [ "Skipping imaginary part computation, use \u001b[1;35mrun_im\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to run it, or \u001b[1;35mcompute_rad\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m to compute the radiative rates of \n", "selected eigenmodes\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "path = lattice.bz_path(['G', 'M', 'K', 'G'], [30, 20, 30])\n", "numeig, verbose = 20, True\n", "\n", "gme.run(kpoints=path['kpoints'], gmode_inds=[0, 3], numeig=numeig, verbose=True, compute_im=False)\n", "freqs_xyeven = gme.freqs\n", "nkappa, nfreq = freqs_xyeven.shape[0], freqs_xyeven.shape[1]\n", "print(f'\\n Number of wavevectors = {nkappa}, number of frequencies = {nfreq}\\n')\n", "\n", "gme.run(kpoints=path['kpoints'], gmode_inds=[1, 2], numeig=numeig, verbose=True, compute_im=False)\n", "freqs_xyodd = gme.freqs\n", "\n", "# X0 is the 1D array with wavevectors, X is the 2D array used for the scatter plot\n", "(X0,X) = legume.viz.calculate_x(path[\"kpoints\"],numeig,k_units=True) # Create an array for bands plotting" ] }, { "cell_type": "markdown", "metadata": { "id": "kKWoGfRrC77t" }, "source": [ "## Plot the results and comparison" ] }, { "cell_type": "markdown", "metadata": { "id": "zie8JCSC1Rcz" }, "source": [ "Calculate the cladding light line for the plot (the one corresponding to the higher permittivity, but here the two claddings are identical)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "executionInfo": { "elapsed": 244, "status": "ok", "timestamp": 1708441444119, "user": { "displayName": "SIMONE ZANOTTI", "userId": "14051006727806237496" }, "user_tz": -60 }, "id": "ce1O-Pt71Pql", "tags": [] }, "outputs": [], "source": [ "eps_clad = [gme.phc.claddings[0].eps_avg, gme.phc.claddings[-1].eps_avg]\n", "vec_LL = np.sqrt(\n", " np.square(gme.kpoints[0, :]) +\n", " np.square(gme.kpoints[1, :])) / 2 / np.pi / np.sqrt(max(eps_clad))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We make the plots explicitly, without using `legume.viz.bands`, by making a scatter plot of frquency vs wavevector. This requires extracting a 2D array, with the same shape of gme.r\n", "in which the wavevector is normalized from 0 to 1. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 428 }, "executionInfo": { "elapsed": 933, "status": "ok", "timestamp": 1708441498542, "user": { "displayName": "SIMONE ZANOTTI", "userId": "14051006727806237496" }, "user_tz": -60 }, "id": "AMQanf-y2AX_", "outputId": "274c26bf-0fb4-4386-e567-3d7345d3467e", "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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