{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d4dada7",
   "metadata": {},
   "source": [
    "# Design of a thin-film lithium niobate photonic crystal Bragg reflector for Fabry–Pérot microresonators\n",
    "\n",
    "**Author: Taekyung Kim, Korea Advanced Institute of Science & Technology (KAIST)**\n",
    "\n",
    "Photonic crystal (PhC) Bragg reflectors use a periodic modulation of a waveguide to couple forward- and backward-propagating modes over a designed spectral range. In thin-film lithium niobate (TFLN), these structures can serve as compact mirrors for Fabry–Pérot microresonators while retaining the electro-optic and nonlinear properties of lithium niobate.\n",
    "\n",
    "In this notebook, we use Tidy3D to analyze a fishbone-shaped TFLN PhC reflector in three steps: we calculate the Bloch-mode band structure of a periodic unit cell, connect the access-waveguide effective index to the Bragg period, and evaluate reflection and guided-mode leakage in a finite apodized reflector.\n",
    "\n",
    "> **Reference:** H. Hwang *et al.*, “High-Q photonic crystal Fabry–Pérot micro-resonator in thin-film lithium niobate,” *APL Photonics* **10**, 126103 (2025). DOI: [10.1063/5.0284518](https://doi.org/10.1063/5.0284518).\n",
    "\n",
    "<img src=\"img/TFLNFabryPerot.png\" alt=\"diagram\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c663da",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a665190d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins import waveguide\n",
    "from tidy3d.plugins.resonance import ResonanceFinder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbb90a6",
   "metadata": {},
   "source": [
    "The lithium-niobate optical axis is oriented along the $y$ direction, corresponding to the dominant $E_y$ component of the quasi-TE mode considered here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b84690f",
   "metadata": {},
   "outputs": [],
   "source": [
    "silica = td.material_library[\"SiO2\"][\"Palik_NoLoss\"]\n",
    "ln = td.material_library[\"LiNbO3\"][\"Zelmon1997\"](1)\n",
    "air = td.Medium(permittivity=1.0)\n",
    "\n",
    "# Baseline geometry\n",
    "target_wavelength_um = 1.56\n",
    "film_thickness_um = 0.5\n",
    "etch_depth_um = 0.3\n",
    "slab_thickness_um = film_thickness_um - etch_depth_um\n",
    "period_um = 0.445\n",
    "backbone_width_um = 0.200\n",
    "corrugation_width_um = 0.500\n",
    "duty_cycle = 0.35\n",
    "sidewall_angle = np.deg2rad(20)\n",
    "\n",
    "# Band-structure source and time settings\n",
    "freq0 = td.C_0 / target_wavelength_um\n",
    "fwidth = freq0 / 20\n",
    "run_time = 100 / fwidth\n",
    "t_start = 5 / fwidth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ded002",
   "metadata": {},
   "source": [
    "## Photonic bandgap of the fishbone unit cell\n",
    "\n",
    "A one-dimensional periodic perturbation folds the guided-wave dispersion into the first Brillouin zone. Near the zone boundary, $k/(2\\pi/a)=0.5$, counter-propagating Bloch waves become Bragg coupled. The periodic corrugation lifts their degeneracy and opens a photonic bandgap whose center and width depend on the average modal index and on the strength of the periodic perturbation. Here, the unit-cell parameters are chosen to place the bandgap center near 1.56 $\\mathrm{\\mu m}$.\n",
    "\n",
    "The unit cell consists of a narrow central backbone and symmetric corrugation teeth, with the tooth length set by the duty cycle. Randomly positioned $E_y$ dipoles excite the Bloch modes, while `FieldTimeMonitor`s record the ring-down signals. Bloch boundary conditions are applied along the propagation direction, and the resonant frequencies are extracted using `ResonanceFinder` as the Bloch wavevector is swept around the Brillouin-zone edge.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "700412f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_unit_cell_simulation(period, film_thickness, etch_depth, backbone_width, corrugation_width, bloch_k):\n",
    "    slab_thickness = film_thickness - etch_depth\n",
    "    tooth_length = period * duty_cycle\n",
    "    y_inner = backbone_width / 2\n",
    "    y_outer = y_inner + corrugation_width\n",
    "\n",
    "    structures = [\n",
    "        td.Structure(geometry=td.PolySlab(vertices=[(-100, y_inner), (100, y_inner), (100, -y_inner), (-100, -y_inner)],axis=2, slab_bounds=(slab_thickness, film_thickness),\n",
    "            sidewall_angle=sidewall_angle, reference_plane=\"top\"), medium=ln),\n",
    "        td.Structure(geometry=td.Box(center=(0, 0, slab_thickness / 2), size=(td.inf, td.inf, slab_thickness)), medium=ln),\n",
    "        td.Structure(geometry=td.Box(center=(0, 0, -50), size=(td.inf, td.inf, 100)), medium=silica)]\n",
    "\n",
    "    for vertices in [\n",
    "        [(-tooth_length / 2, y_inner), (tooth_length / 2, y_inner),\n",
    "            (tooth_length / 2, y_outer), (-tooth_length / 2, y_outer)],\n",
    "        [(-tooth_length / 2, -y_inner), (tooth_length / 2, -y_inner),\n",
    "            (tooth_length / 2, -y_outer), (-tooth_length / 2, -y_outer)]\n",
    "            ]:\n",
    "        structures.append(\n",
    "            td.Structure(\n",
    "                geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(slab_thickness, film_thickness),\n",
    "                sidewall_angle=sidewall_angle, reference_plane=\"top\"), medium=ln))\n",
    "\n",
    "    rng = np.random.default_rng(12345)\n",
    "\n",
    "    dipole_positions = rng.uniform(\n",
    "        [-1.2 * tooth_length / 2, 0, slab_thickness + etch_depth / 2],\n",
    "        [1.2 * tooth_length / 2, y_outer, slab_thickness + etch_depth / 2],\n",
    "        [10, 3])\n",
    "\n",
    "    dipole_phases = rng.uniform(0, 2 * np.pi, 10)\n",
    "\n",
    "    monitor_positions = rng.uniform(\n",
    "        [-1.2 * tooth_length / 2, 0, slab_thickness + etch_depth / 2],\n",
    "        [1.2 * tooth_length / 2, y_outer, slab_thickness + etch_depth / 2],\n",
    "        [4, 3])\n",
    "\n",
    "    sources = []\n",
    "    for i in range(10):\n",
    "        sources.append(\n",
    "            td.PointDipole(\n",
    "                center=tuple(dipole_positions[i]), polarization=\"Ey\",\n",
    "                source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth, phase=dipole_phases[i]),\n",
    "                name=f\"dipole_{i}\",\n",
    "            )\n",
    "        )\n",
    "\n",
    "    monitors = []\n",
    "    for i in range(4):\n",
    "        monitors.append(\n",
    "            td.FieldTimeMonitor(\n",
    "                center=tuple(monitor_positions[i]), size=(0, 0, 0), fields=[\"Ey\"],\n",
    "                start=t_start, name=f\"monitor_time_{i}\",\n",
    "            )\n",
    "        )\n",
    "\n",
    "    return td.Simulation(\n",
    "        center=(0, 0, film_thickness / 2),\n",
    "        size=(period, backbone_width + 2 * corrugation_width + 1.5 + period, 2 * 1.5 + film_thickness),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20),\n",
    "        structures=structures, sources=sources, monitors=monitors,\n",
    "        run_time=run_time, shutoff=0, normalize_index=None, symmetry=(0, -1, 0), medium=air,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.bloch(bloch_k), y=td.Boundary.absorber(), z=td.Boundary.pml()\n",
    "        ),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "388637d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_k = 11\n",
    "k_values = np.linspace(0.475, 0.525, num_k)\n",
    "\n",
    "band_simulations = {}\n",
    "for i, bloch_k in enumerate(k_values):\n",
    "    band_simulations[f\"sim_{i}\"] = make_unit_cell_simulation(\n",
    "        period_um, film_thickness_um, etch_depth_um,\n",
    "        backbone_width_um, corrugation_width_um, bloch_k,\n",
    "    )\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "band_simulations[\"sim_0\"].plot(z=film_thickness_um - 0.01, ax=axes[0])\n",
    "band_simulations[\"sim_0\"].plot(x=0, freq=freq0, ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655bd36b",
   "metadata": {},
   "source": [
    "### Run the band-structure simulations\n",
    "\n",
    "The simulations for different Bloch wavevectors are independent and are submitted together as a `Batch`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d7a3ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4454ae20775b4b6d88010d17aa47e58a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:42:23 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:42:23 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m11\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:42:35 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.368</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:42:35 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.368\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22cca118a4134a35be3b0212736a0bd9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:43:29 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:43:29 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "band_batch = web.Batch(simulations=band_simulations, verbose=True)\n",
    "band_results = band_batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163862fc",
   "metadata": {},
   "source": [
    "### Extract and plot the band structure\n",
    "\n",
    "`ResonanceFinder` is used to extract the Bloch-mode frequencies from the time-domain monitor signals at each wavevector. Resonances with small amplitudes or large fitting errors are removed before plotting the band structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "565ee0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZonvuuUexsbHq0qWL2rdvbx55LTJv3jzVqVNHHTp00AMPPKBBgwapevXq5r/vW7Roobfeekuvv/66brvtNn3yySeaOHFisXX5+vpq5MiRevjhh3X33XerWrVq+vzzz0utzWq1auXKlQoODtb999+vZs2aadKkSXJ3d5d04cDU0qVLtXLlSrVp00Z33nmn3n77bfOIZFnccMMNGjdunEaNGqU6dero8ccflySNHz9eL774oiZOnKimTZsqOjpay5YtU4MGDS7bX1pamvbt23fZNtu2bVNkZKQiIyMlSSNGjFBkZKR5fVd3d3ft2rVLPXr00M0336yBAweqVatW2rBhg00AnTJliuLi4tS3b1+1adNGR44c0Zo1a8zxy+7u7lq6dKnc3d3Vrl07Pfroo+rXr59eeeWVMr8+lZ3FMAzD2UVIF/5lsmDBAvOOFMePH9cNN9ygPn366NNPPzXb9ejRQ35+fvrss8+0ePFiPfvss9qxY4c5uPjSfgYPHqwjR47o22+/NfvIysqSn5+fvvnmm8sObbhYenq6AgIClJaWJn9///LZaAAAXNCxY8cUHh5unuBVFnPmzNHTTz9doXfQgmuyJ3NV2iOyQUFB8vDwKDaGo2nTpuah9zVr1ujgwYOqUaOGPDw85OFxYcjvgw8+aP7FGhISYg5sLlLWgc4AAODC9+3ixYt16NAhbdq0SXFxcapfv746dOjg7NJQxVXaIGu1WtWmTZtih+f3799v/stg1KhR2rVrlznOpGisydtvv63Zs2dLujDQ+eeff7a5/MP1NtAZAABHys/P19///nfdeuuteuCBB1S7dm0lJCQUO0sdqGhOvWpBRkaGDhw4YE4XDXYODAxU3bp1FR8fr4ceekgdOnRQp06dtGLFCi1ZssS8hlrRLdsuVbduXXMMy8UDnSdPnqyUlJTrbqAzAACOFBUVZZ5kdLUGDBhg3v4VKC9OPSJ7pcHODzzwgKZPn67JkyerWbNmmjVrlr766ivzMhNlURUGOgMAAFRFleZkr8qOk70AAAAc77o42QsAAAC4HIIsAAAAXBJBFgAAAC6JIFsJZefl6Mjvx5Wdl+PsUgAAQBVXmXOJUy+/BVsFhQWasmSmZqyer+y8HPlYvTWkc5ziYwfJ3c3d2eUBAIAqxBVyCUG2EpmyZKamLZ+jostIZOflaNryOZKkUT2HOq0uAABQ9bhCLmFoQSWRnZejGavn69JroRmSPvjjLyEAAICK4Cq5hCBbSaSmny71lyIrL0ep6acruCIAAFBVuUouIchWEsH+gfKxepe4zNfqrWD/wAquCAAAVFWukksIspVE0QBqyyXzLZIGd44r9ZcJAACgvLlKLuFkr0okPnaQpAtjT7LycuRr9dbgP84OBAAAqEiukEsshmFcOo4XJbDnvr/XKvuPsSeXO6wPAABQESo6l9iTuTgiWwn5WL1VLyjM2WUAAABU6lzCGFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyqFDZeTk68vtxZeflOLsUAADKDd9vzuHh7AJQNRQUFmjKkpmasXq+svNy5GP11pDOcYqPHSR3N3dnlwcAwFXh+825CLKoEFOWzNS05XNk/DGdnZejacvnSJJG9RzqtLoAALgWfL85F0ML4HDZeTmasXq++SYvYkj64I+/YAEAcDV8vzkfQRYOl5p+utQ3c1ZejlLTT1dwRQAAXDu+35yPIAuHC/YPlI/Vu8RlvlZvBfsHVnBFAABcO77fnM+pQXb9+vWKjY1VWFiYLBaLFi5cWKzN3r171aNHDwUEBMjPz09t2rRRUlKSJOn06dN64okn1KRJE/n4+Khu3bp68sknlZaWZtNHUlKSunXrJl9fXwUHBys+Pl7nz5+viE2EZA58t1wy3yJpcOe4Uj8EAACozPh+cz6nnuyVmZmpFi1a6LHHHlPv3r2LLT948KDat2+vgQMHaty4cfL399eePXvk7X3hF+P48eM6fvy43njjDUVEROjIkSMaOnSojh8/ri+//FKSVFBQoG7duikkJESbNm1ScnKy+vXrJ09PT7322msVur1VWXzsIEkXxgxl5eXI1+qtwX+c1QkAgKvi+825LIZhXDpG2SksFosWLFigXr16mfPi4uLk6empjz76qMz9fPHFF3r00UeVmZkpDw8PLV++XN27d9fx48dVp04dSdL06dM1cuRInTx5UlartcR+cnNzlZuba06np6crPDxcaWlp8vf3v7qNhLL/GDN0uX/HAADgavh+Kz/p6ekKCAgoU+aqtGNkCwsLtWzZMt18882KiopScHCw2rZtW+Lwg4sVbbSHx4WDzZs3b1azZs3MECtJUVFRSk9P1549e0rtZ+LEiQoICDAf4eHh5bJdVZ2P1Vv1gsJ4kwMArit8vzlHpQ2yqampysjI0KRJkxQdHa2VK1fqgQceUO/evbVu3boSn/P7779r/PjxGjx4sDkvJSXFJsRKMqdTUlJKXf/o0aOVlpZmPo4ePVoOWwUAAIDyUmlviFBYWChJ6tmzp5555hlJUsuWLbVp0yZNnz5dHTt2tGmfnp6ubt26KSIiQmPHjr3m9Xt5ecnLy+ua+wEAAIBjVNojskFBQfLw8FBERITN/KZNm5pXLShy7tw5RUdHq3r16lqwYIE8PT3NZSEhITpx4oRN+6LpkJAQB1UPAAAAR6u0QdZqtapNmzbat2+fzfz9+/erXr165nR6erq6du0qq9WqxYsXm1c0KNKuXTv9/PPPSk1NNeetWrVK/v7+xUIyAAAAXIdThxZkZGTowIED5vShQ4eUmJiowMBA1a1bV/Hx8XrooYfUoUMHderUSStWrNCSJUuUkJAg6X8hNisrSx9//LHS09OVnp4uSapdu7bc3d3VtWtXRUREqG/fvpo8ebJSUlI0ZswYDR8+nKEDAAAALsypl99KSEhQp06dis3v37+/5syZI0n68MMPNXHiRB07dkxNmjTRuHHj1LNnz8s+X7oQiuvXry9JOnLkiIYNG6aEhAT5+fmpf//+mjRpknllg7Kw51IQAAAAuDr2ZK5Kcx3Zyo4gCwAA4HjXxXVkAQAAgMshyAIAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgiwAAABcEkEWKCfZeTk68vtxZeflOLsUAKiU+JxEeXPqnb2A60FBYYGmLJmpGavnKzsvRz5Wbw3pHKf42EFyd3N3dnkA4HR8TsJRCLLANZqyZKamLZ+jojuLZOflaNryOZKkUT2HOq0uAKgs+JyEozC0ALgG2Xk5mrF6vi69PZ4h6YM/jjwAQFXG5yQciSALXIPU9NOlfghn5eUoNf10BVcEAJULn5NwJIIscA2C/QPlY/UucZmv1VvB/oEVXBEAVC58TsKRCLLANSg6YcFyyXyLpMGd40r98AaAqoLPSTgSJ3sB1yg+dpCkC2O9svJy5Gv11uA/zsYFAPA5CcexGIZx6fhrlCA9PV0BAQFKS0uTv7+/s8tBJZT9x1ivy/0bDQCqMj4nURb2ZC6OyALlxMfqrXpBYc4uAwAqLT4nUd4YIwsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSAL4Iqy83J05Pfjys7LcXYpwHWP9xtQdtzZC0CpCgoLNGXJTM1YPV/ZeTnysXpryB/3R3d3c3d2ecB1hfcbYD+CLIBSTVkyU9OWz5Hxx3R2Xo6mLZ8jSRrVc6jT6gKuR7zfAPsxtABAibLzcjRj9XzzS7WIIemDP44YASgfvN+Aq0OQBVCi1PTTpX55ZuXlKDX9dAVXBFy/eL8BV4cgC6BEwf6B8rF6l7jM1+qtYP/ACq4IuH7xfgOuDkEWQImKTjSxXDLfImlw57hSv3QB2I/3G3B1ONkLQKniYwdJujBGLysvR75Wbw3+4yxqAOWL9xtgP4thGJeOLUcJ0tPTFRAQoLS0NPn7+zu7HKBCZf8xRu9y//4EUD54v6GqsydzcUQWwBX5WL1VLyjM2WUAVQLvN6DsnDpGdv369YqNjVVYWJgsFosWLlxYrM3evXvVo0cPBQQEyM/PT23atFFSUpK5PCcnR8OHD1etWrVUrVo1Pfjggzpx4oRNH0lJSerWrZt8fX0VHBys+Ph4nT9/3tGbBwAAAAdyapDNzMxUixYt9N5775W4/ODBg2rfvr1uueUWJSQkaNeuXXrxxRfl7f2/f7U888wzWrJkib744gutW7dOx48fV+/evc3lBQUF6tatm/Ly8rRp0ybNnTtXc+bM0UsvveTw7QMAAIDjVJoxshaLRQsWLFCvXr3MeXFxcfL09NRHH31U4nPS0tJUu3Ztffrpp/q///s/SdJ//vMfNW3aVJs3b9add96p5cuXq3v37jp+/Ljq1KkjSZo+fbpGjhypkydPymq1lth3bm6ucnNzzen09HSFh4czRhYAAMCB7BkjW2kvv1VYWKhly5bp5ptvVlRUlIKDg9W2bVub4Qfbt29Xfn6+unTpYs675ZZbVLduXW3evFmStHnzZjVr1swMsZIUFRWl9PR07dmzp9T1T5w4UQEBAeYjPDy8/DcSgNNl5+XoyO/HuXOSi2G/AZAqcZBNTU1VRkaGJk2apOjoaK1cuVIPPPCAevfurXXr1kmSUlJSZLVaVaNGDZvn1qlTRykpKWabi0Ns0fKiZaUZPXq00tLSzMfRo0fLcesAOFtBYYEmLZquiOei1XZMb0U8F61Ji6aroLDA2aXhMthvAC5Waa9aUFhYKEnq2bOnnnnmGUlSy5YttWnTJk2fPl0dO3Z06Pq9vLzk5eXl0HUAcJ4pS2Zq2vI55r3ts/NyNG35HEnSqJ5DnVYXLo/9BuBilfaIbFBQkDw8PBQREWEzv2nTpuZVC0JCQpSXl6ezZ8/atDlx4oRCQkLMNpdexaBouqgNgKolOy9HM1bP16UnCBi6cDF6/l1dObHfAFyq0gZZq9WqNm3aaN++fTbz9+/fr3r16kmSWrVqJU9PT61evdpcvm/fPiUlJaldu3aSpHbt2unnn39Wamqq2WbVqlXy9/cvFpIBVA2p6adLDT1Zf1yMHpUP+w3ApZw6tCAjI0MHDhwwpw8dOqTExEQFBgaqbt26io+P10MPPaQOHTqoU6dOWrFihZYsWaKEhARJUkBAgAYOHKgRI0YoMDBQ/v7+euKJJ9SuXTvdeeedkqSuXbsqIiJCffv21eTJk5WSkqIxY8Zo+PDhDB0AqqiiOyaVFIp8rd4K9g90QlXl73q7Q1RV2W8Ays6pR2S3bdumyMhIRUZGSpJGjBihyMhI8xqvDzzwgKZPn67JkyerWbNmmjVrlr766iu1b9/e7OPtt99W9+7d9eCDD6pDhw4KCQnR119/bS53d3fX0qVL5e7urnbt2unRRx9Vv3799Morr1TsxgKoNHys3hrSOU6WS+ZbJA3uHOfyoe96PSHqet9vAOxXaa4jW9nZc00zAJVfQWGBpiyZqQ9Wz1dWXo58rd4a3DlO8bGD5O7m7uzyrsmkRdNtToiSLoS9p2IGuPwJUdfzfgNwgT2ZiyBbRgRZ4Pp0vf37PTsvRxHPRZf67/c9b6y4brbzetpvAP7nurghAgBUBB+rt+oFhV03YaiqnBB1ve03AFeHIAsA15HLHaHkhCgA1xuCLABcRzghCkBVUmnv7AUAuDrxsYMkqcQTogDgesLJXmXEyV4AXA0nRAFwRfZkLo7IAsB1quiEKAC4XjFGFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJfkYU/js2fPasGCBdqwYYOOHDmirKws1a5dW5GRkYqKitJdd93lqDoBAAAAG2U6Inv8+HH97W9/U2hoqF599VVlZ2erZcuW6ty5s2688UatXbtWf/rTnxQREaHPP//c0TUDAAAAZTsiGxkZqf79+2v79u2KiIgosU12drYWLlyoqVOn6ujRo3ruuefKtVAAAADgYhbDMIwrNTp16pRq1apV5k7tbe8K0tPTFRAQoLS0NPn7+zu7HAAAgOuSPZmrTEML7A2l11uIBQAAQOVj18leRY4fP66NGzcqNTVVhYWFNsuefPLJcikMAAAAuBy7g+ycOXM0ZMgQWa1W1apVSxaLxVxmsVgIsgAAAKgQZRoje7Hw8HANHTpUo0ePlptb1bkMLWNkAQAAHK/cx8heLCsrS3FxcVUqxAIAAKDysTuNDhw4UF988UW5rHz9+vWKjY1VWFiYLBaLFi5caLN8wIABslgsNo/o6GibNvv371fPnj0VFBQkf39/tW/fXmvXrrVpk5SUpG7dusnX11fBwcGKj4/X+fPny2UbAAAA4Bx2j5GdOHGiunfvrhUrVqhZs2by9PS0Wf7WW2+Vua/MzEy1aNFCjz32mHr37l1im+joaM2ePduc9vLyslnevXt33XTTTVqzZo18fHw0depUde/eXQcPHlRISIgKCgrUrVs3hYSEaNOmTUpOTla/fv3k6emp1157zY4tBwAAQGVyVUH222+/VZMmTSSp2Mle9oiJiVFMTMxl23h5eSkkJKTEZb///rt+/fVX/etf/1Lz5s0lSZMmTdI///lP7d69WyEhIVq5cqV++eUXfffdd6pTp45atmyp8ePHa+TIkRo7dqysVmuJfefm5io3N9ecTk9Pt2vbAAAA4Fh2Dy1488039eGHH2rv3r1KSEjQ2rVrzceaNWvKvcCEhAQFBwerSZMmGjZsmE6dOmUuq1Wrlpo0aaJ58+YpMzNT58+f14wZMxQcHKxWrVpJkjZv3qxmzZqpTp065vOioqKUnp6uPXv2lLreiRMnKiAgwHyEh4eX+7YBAADg6tkdZL28vHT33Xc7opZioqOjNW/ePK1evVqvv/661q1bp5iYGBUUFEi6cAT4u+++044dO1S9enV5e3vrrbfe0ooVK1SzZk1JUkpKik2IlWROp6SklLru0aNHKy0tzXwcPXrUQVsJAACAq2H30IKnnnpK7777rt555x1H1GMjLi7O/LlZs2Zq3ry5GjVqpISEBHXu3FmGYWj48OEKDg7Whg0b5OPjo1mzZik2NlZbt25VaGjoVa/by8ur2HhcAAAAVB52B9ktW7ZozZo1Wrp0qW699dZiJ3t9/fXX5VbcpRo2bKigoCAdOHBAnTt3Nus4c+aMeZ2xf/7zn1q1apXmzp2rUaNGKSQkRFu2bLHp58SJE5JU6thbAAAAVH52B9kaNWqUeoUBRzt27JhOnTplHmnNysqSpGLXtHVzczNvnduuXTtNmDBBqampCg4OliStWrVK/v7+ioiIqMDqAQAAUJ7sDrIXXwrrWmVkZOjAgQPm9KFDh5SYmKjAwEAFBgZq3LhxevDBBxUSEqKDBw/q+eefV+PGjRUVFSXpQkitWbOm+vfvr5deekk+Pj6aOXOmDh06pG7dukmSunbtqoiICPXt21eTJ09WSkqKxowZo+HDhzN0AAAAwIU59fZc27ZtU2RkpCIjIyVJI0aMUGRkpF566SW5u7tr165d6tGjh26++WYNHDhQrVq10oYNG8wAGhQUpBUrVigjI0P33XefWrdurY0bN2rRokVq0aKFJMnd3V1Lly6Vu7u72rVrp0cffVT9+vXTK6+84rTtBgAAwLWzGIZh2POEEydO6LnnntPq1auVmpqqS59edEWB64099/0FAADA1bEnc9k9tGDAgAFKSkrSiy++qNDQULtvggAAAACUB7uD7MaNG7Vhwwa1bNnSAeUAAAAAZWP3GNnw8PBiwwkAAACAimZ3kJ06dapGjRqlw4cPO6AcAAAAoGzsHlrw0EMPKSsrS40aNZKvr2+xGyKcPn263IoDAAAASmN3kJ06daoDygAAAADsY3eQ7d+/vyPqAAAAAOxSpjGymZmZdnVqb3sAAADAXmUKso0bN9akSZOUnJxcahvDMLRq1SrFxMTonXfeKbcCAQAAgJKUaWhBQkKC/v73v2vs2LFq0aKFWrdurbCwMHl7e+vMmTP65ZdftHnzZnl4eGj06NEaMmSIo+sGAABAFWfXLWqTkpL0xRdfaMOGDTpy5Iiys7MVFBSkyMhIRUVFKSYmRu7u7o6s12m4RS0AAIDj2ZO57AqyVRlBFgAAwPHsyVx23xABAAAAqAwIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC7J7lvUStLZs2e1ZcsWpaamqrCw0GZZv379yqUwAAAA4HLsDrJLlizRI488ooyMDPn7+8tisZjLLBYLQRYAAAAVwu6hBc8++6wee+wxZWRk6OzZszpz5oz5OH36tCNqBAAAAIqxO8j+9ttvevLJJ+Xr6+uIegAAAIAysTvIRkVFadu2bY6oBQAAACizMo2RXbx4sflzt27dFB8fr19++UXNmjWTp6enTdsePXqUb4UAAABACSyGYRhXauTmVrYDtxaLRQUFBddcVGVkz31/AQAAcHXsyVxlOiJ76SW2AAAAAGeze4zsvHnzlJubW2x+Xl6e5s2bVy5FAQAAAFdSpqEFF3N3d1dycrKCg4Nt5p86dUrBwcEMLQAAAMBVsydz2X1E1jAMm5sgFDl27JgCAgLs7Q4AAAC4KmW+s1dkZKQsFossFos6d+4sD4//PbWgoECHDh1SdHS0Q4oEAAAALlXmINurVy9JUmJioqKiolStWjVzmdVqVf369fXggw+We4EAAABAScocZF9++WVJUv369fXQQw/J29vbYUUBAAAAV1LmIFukf//+jqgDAAAAsIvdQbZmzZolnuxlsVjk7e2txo0ba8CAAfrrX/9aLgUCAAAAJbE7yL700kuaMGGCYmJidMcdd0iStmzZohUrVmj48OE6dOiQhg0bpvPnz2vQoEHlXjAAAAAgXUWQ3bhxo1599VUNHTrUZv6MGTO0cuVKffXVV2revLneeeedKwbZ9evXa8qUKdq+fbuSk5O1YMEC86QySRowYIDmzp1r85yoqCitWLHCZt6yZcv0yiuvaNeuXfL29lbHjh21cOFCc3lSUpKGDRumtWvXqlq1aurfv78mTpxoc+WFssrMzZZ7rmex+e5ubvL29LJpVxo3i0U+Vu+rapuVl6PSLv1rsVjke5Vts/NyVHiZSwr7eflcVduc/FwVXObOcPa09bV6m/8NyM3P0/nC0q9ZbE9bH08v8zbMeefzlV9wvlzaenta5e7mbnfb/ILzyjufX2pbLw9Pebh72N32fMF55V6mrdXDU55X0bagsEA5+XmltvV095DVw9PutoWFhcrOL37zlatp6+HmLi9Pq6QLlxDMysspl7b2vO/5jCi5LZ8RfEbwGWF/2+v9M8Iedie5b7/9Vq+//nqx+Z07d9azzz4rSbr//vs1atSoK/aVmZmpFi1a6LHHHlPv3r1LbBMdHa3Zs2eb015eXjbLv/rqKw0aNEivvfaa7rvvPp0/f167d+82lxcUFKhbt24KCQnRpk2blJycrH79+snT01OvvfZambb5Yi1GdpObtfjL1vm2u/TJ42+Z07fFxyi7lF/udjdFasGz75vTbV54QKczzpa8vnpN9e3o/21/h7FxOnY6pcS2N4c20PqXPzOnoyf+VfuTD5XY9sbAEG17baE53evNYdp5ZG+JbQOr1dAvb/zvj4eH331Gm3/dUWJbH6u3Dr2TYE4PnDFaq3dvKrGtJKVM/8H8+fHZ47T0pzWltj04ba35Roj/ZJL+/cM3pbbdPWW5gqrXlCS9/OU0zVn3Valtt7z6teoGhUmSJi6arvdXfVJq24SXPtUtYQ0lSdOWz9Gby/5Vatvloz5UZP0ISdLMNZ9r/Nf/KLXtV8+8p7ubtJIkfbRhof4+/41S2340/E39qdndF5734wo9Pe/VUtt+MGiCerTqLEn6JnGdBs98odS2U/uNUdxd3SVJa3/5UX3fe7bUtq/FPafH7v0/SdIPvybqwbeHl9r2xd6Pa3jXRyVJu5L2KWbSY6W2fbbbQMXHXvgDeH/KYd37ysOlth32p0f08oNPSJKOnU7RHWNK/gyRpAEdH9SkPvGSpFMZZ3VbfEypbf9y5/16Z8BLki58iDd6qlOpbbvffp9mDf7f58jl2vIZcQGfEf/DZ8QFfEZcwGfEBT5Wb+18dXGJy0pid5ANDAzUkiVL9Mwzz9jMX7JkiQIDAyVdCKjVq1e/Yl8xMTGKiSn9l0W6EFxDQkJKXHb+/Hk99dRTmjJligYOHGjOj4iIMH9euXKlfvnlF3333XeqU6eOWrZsqfHjx2vkyJEaO3asrFZriX3n5uba3Io3PT39itsDAACAimP3LWpnzpypYcOG6f777zfHyG7dulXffPONpk+froEDB+rNN9/Uli1b9Pnnn5e9EIulxKEFCxculNVqVc2aNXXffffp1VdfVa1atSRdGJvbtm1bffjhh3rnnXeUkpKili1basqUKbrtttskXRjTu3jxYiUmJpr9Hjp0SA0bNtRPP/2kyMjIEusZO3asxo0bV2z+8dSUEm+Xxr8ESm7Lvw35tyH/NrS/LZ8RV9eWz4gL+Iywvy2fERdUls+Igtz8Mt+i1u4gK0nff/+9/vGPf2jfvn2SpCZNmuiJJ57QXXfdZW9X/yukhCA7f/58+fr6qkGDBjp48KD+/ve/q1q1atq8ebPc3d01f/589enTR3Xr1tVbb72l+vXr680339TKlSu1f/9+BQYGavDgwTpy5Ii+/fZbs9+srCz5+fnpm2++KfWIcElHZMPDw8v0ogIAAODqpKenlznI2n+2k6S7775bd99991UVZ4+4uDjz52bNmql58+Zq1KiREhIS1LlzZxX+8Zf5Cy+8YN5VbPbs2brxxhv1xRdfaMiQIVe9bi8vr2LjcQEAAFB5XFWQLSws1IEDB5SammqGySIdOnQol8JK0rBhQwUFBenAgQPq3LmzQkNDJdmOifXy8lLDhg2VlJQkSQoJCdGWLVts+jlx4oS5DAAAAK7J7iD7ww8/6OGHH9aRI0eKjZuwWCwqKCh9nNG1OnbsmE6dOmUG2FatWsnLy0v79u1T+/btJUn5+fk6fPiw6tWrJ0lq166dJkyYoNTUVAUHB0uSVq1aJX9/f5sADAAAANdid5AdOnSoWrdurWXLlik0NLTEu3yVVUZGhg4cOGBOHzp0SImJiQoMDFRgYKDGjRunBx98UCEhITp48KCef/55NW7cWFFRUZIkf39/DR06VC+//LLCw8NVr149TZkyRZL05z//WZLUtWtXRUREqG/fvpo8ebJSUlI0ZswYDR8+nKEDAAAALszuk738/Py0c+dONW7c+JpXnpCQoE6dil9TrX///nr//ffVq1cv7dixQ2fPnlVYWJi6du2q8ePHq06dOmbb/Px8jR49Wh999JGys7PVtm1bTZ06VbfeeqvZ5siRIxo2bJgSEhLk5+en/v37a9KkSXbdEMGegccAAAC4OvZkLruD7H333afnn39e0dHR11SkqyHIAgAAOJ5Dr1rwxBNP6Nlnn1VKSoqaNWsmT0/b27U2b97c3i4BAAAAu9l9RLboQs82nVgsMgzD4Sd7ORNHZAEAABzPoUdkDx0q+Z67AAAAQEWyO8gWXdYKAAAAcKbi4wTK4KOPPtLdd9+tsLAwHTlyRJI0depULVq0qFyLAwAAAEpjd5B9//33NWLECN1///06e/asOSa2Ro0amjp1annXBwAAAJTI7iD77rvvaubMmXrhhRfk7u5uzm/durV+/vnnci0OAAAAKI3dQfbQoUOKjIwsNt/Ly0uZmZnlUhQAAABwJXYH2QYNGigxMbHY/BUrVqhp06blURMAAABwRXZftWDEiBEaPny4cnJyZBiGtmzZos8++0wTJ07UrFmzHFEjAAAAUIzdQfZvf/ubfHx8NGbMGGVlZenhhx9WWFiYpk2bpri4OEfUCAAAABRj9529LpaVlaWMjAwFBweXZ02VEnf2AgAAcDyH3tnrYr6+vvL19b2WLgAAAICrUqYgGxkZKYvFUqYOf/rpp2sqCAAAACiLMgXZXr16ObgMAAAAwD7XNEa2KmGMLAAAgOPZk7nsvo6sJJ09e1azZs3S6NGjdfr0aUkXhhT89ttvV9MdAAAAYDe7T/batWuXunTpooCAAB0+fFiDBg1SYGCgvv76ayUlJWnevHmOqBMAAACwYfcR2REjRmjAgAH69ddf5e3tbc6///77tX79+nItDgAAACiN3UF269atGjJkSLH5N9xwg1JSUsqlKAAAAOBK7A6yXl5eSk9PLzZ///79ql27drkUBQAAAFyJ3UG2R48eeuWVV5Sfny9JslgsSkpK0siRI/Xggw+We4EAAABASewOsm+++aZ5W9rs7Gx17NhRjRs3VvXq1TVhwgRH1AgAAAAUY/dVCwICArRq1Spt3LhRu3btUkZGhm6//XZ16dLFEfUBAAAAJbL7hghHjx5VeHi4o+qptLghAgAAgOM59IYI9evXV8eOHTVz5kydOXPmqosEAAAAroXdQXbbtm2644479Morryg0NFS9evXSl19+qdzcXEfUBwAAAJTI7iAbGRmpKVOmKCkpScuXL1ft2rU1ePBg1alTR4899pgjagQAAACKsXuMbEl++uknDRw4ULt27VJBQUF51FXpMEYWAADA8Rw6RrbIsWPHNHnyZLVs2VJ33HGHqlWrpvfee+9quwMAAADsYvflt2bMmKFPP/1U33//vW655RY98sgjWrRokerVq+eI+gAAAIAS2R1kX331VfXp00fvvPOOWrRo4YiaAAAAgCuyO8gmJSXJYrE4ohYAAACgzOwOskUhNisrS0lJScrLy7NZ3rx58/KpDAAAALgMu0/2OnnypLp166bq1avr1ltvVWRkpM3DHuvXr1dsbKzCwsJksVi0cOFCm+UDBgyQxWKxeURHR5fYV25urlq2bCmLxaLExESbZbt27dI999wjb29vhYeHa/LkyXbVCQAAgMrH7iD79NNPKy0tTT/++KN8fHy0YsUKzZ07VzfddJMWL15sV1+ZmZlq0aLFZa92EB0dreTkZPPx2Wefldju+eefV1hYWLH56enp6tq1q+rVq6ft27drypQpGjt2rD744AO7agUAAEDlYvfQgjVr1mjRokVq3bq13NzcVK9ePf3pT3+Sv7+/Jk6cqG7dupW5r5iYGMXExFy2jZeXl0JCQi7bZvny5Vq5cqW++uorLV++3GbZJ598ory8PH344YeyWq269dZblZiYqLfeekuDBw8utc/c3Fybu5Wlp6eXYYsAAABQUew+IpuZmang4GBJUs2aNXXy5ElJUrNmzfTTTz+Vb3WSEhISFBwcrCZNmmjYsGE6deqUzfITJ05o0KBB+uijj+Tr61vs+Zs3b1aHDh1ktVrNeVFRUdq3b5/OnDlT6nonTpyogIAA8xEeHl5+GwUAAIBrZneQbdKkifbt2ydJatGihWbMmKHffvtN06dPV2hoaLkWFx0drXnz5mn16tV6/fXXtW7dOsXExJh3DzMMQwMGDNDQoUPVunXrEvtISUlRnTp1bOYVTaekpJS67tGjRystLc18HD16tJy2CgAAAOXB7qEFTz31lJKTkyVJL7/8sqKjo/XJJ5/IarVqzpw55VpcXFyc+XOzZs3UvHlzNWrUSAkJCercubPeffddnTt3TqNHjy7X9UoXhjR4eXmVe78AAAAoH2UOsocOHVKDBg306KOPmvNatWqlI0eO6D//+Y/q1q2roKAghxRZpGHDhgoKCtKBAwfUuXNnrVmzRps3by4WOFu3bq1HHnlEc+fOVUhIiE6cOGGzvGj6SmNvAQAAUHmVOcg2atRI9erVU6dOnXTffffp3nvv1Y033ihfX1/dfvvtjqzRdOzYMZ06dcocwvDOO+/o1VdfNZcfP35cUVFR+vzzz9W2bVtJUrt27fTCCy8oPz9fnp6ekqRVq1apSZMmqlmzZoXUDQAAgPJX5iC7Zs0aJSQkKCEhQZ999pny8vLUsGFD3XffferUqZM6depUbCzqlWRkZOjAgQPm9KFDh5SYmKjAwEAFBgZq3LhxevDBBxUSEqKDBw/q+eefV+PGjRUVFSVJqlu3rk1/1apVk3QhdN94442SpIcffljjxo3TwIEDNXLkSO3evVvTpk3T22+/bVetAAAAqFwshmEY9j4pJydHmzZtMoPtli1blJ+fr1tuuUV79uwpcz8JCQnq1KlTsfn9+/fX+++/r169emnHjh06e/aswsLC1LVrV40fP77UwHz48GE1aNBAO3bsUMuWLc35u3bt0vDhw7V161YFBQXpiSee0MiRI+3a5vT0dAUEBCgtLU3+/v52PRcAAABlY0/muqogWyQvL0/ff/+9li9frhkzZigjI8O8osD1hiALAADgePZkLruuWpCXl6cffvhBa9euVUJCgn788UeFh4erQ4cO+sc//qGOHTteU+EAAABAWZU5yN5333368ccf1aBBA3Xs2FFDhgzRp59+Wu7XjgUAAADKosxBdsOGDQoNDTWvWNCxY0fVqlXLkbUBAAAApSrznb3Onj2rDz74QL6+vnr99dcVFhamZs2a6fHHH9eXX35p3qoWAAAAqAhXfbLXuXPntHHjRnO87M6dO3XTTTdp9+7d5V1jpcDJXgAAAI5nT+Yq8xHZS/n5+ZnXe61Zs6Y8PDy0d+/eq+0OAAAAsEuZx8gWFhZq27ZtSkhI0Nq1a/X9998rMzNTN9xwgzp16qT33nuvxGvCAgAAAI5Q5iBbo0YNZWZmKiQkRJ06ddLbb7+te++9V40aNXJkfQAAAECJyhxkp0yZok6dOunmm292ZD0AAABAmZQ5yA4ZMsSRdQAAAAB2ueqTvQAAAABnIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAsB1KjsvR0d+P67svBxnlwIADlHmW9QCAFxDQWGBpiyZqRmr5ys7L0c+Vm8N6Ryn+NhBcndzd3Z5AFBuCLIAcJ2ZsmSmpi2fI+OP6ey8HE1bPkeSNKrnUKfVBQDljaEFAHAdyc7L0YzV880QW8SQ9MEfR2gB4HpBkAVQpV1v40hT00+Xui1ZeTlKTT9dwRU5xvW23wBcHYYWAKiSrtdxpMH+gfKxepcY8Hyt3gr2D3RCVeXnet1vAK4OR2QBVElF40iLAl/RONIpS2Y6ubJrUxTsLJfMt0ga3DlOPlZvZ5RVbq7X/Qbg6hBkAVQ51/s40vjYQXoqZoB8/witvlZvPRUzQPGxg5xc2bW53vcbAPsxtABAlVOWcaT1gsIquKry4+7mrlE9h+qpmAFKTT9tDjdwddf7fgNgP47IAqhyLhfsrodxpEV8rN6qFxR2XYRYqersNwBlR5AFUOVc7+NIr1fsNwCXYmgBgCqpaLzoB6vnKysvR75Wbw3+4+x3VF7sNwAXsxiGcem4eZQgPT1dAQEBSktLk7+/v7PLAVBOsv8YW3m9jCOtKthvwPXLnszFEVkAVVrROFK4FvYbAIkxsgAAAHBRTg2y69evV2xsrMLCwmSxWLRw4UKb5QMGDJDFYrF5REdHm8sPHz6sgQMHqkGDBvLx8VGjRo308ssvKy8vz6afXbt26Z577pG3t7fCw8M1efLkitg8AAAAOJBThxZkZmaqRYsWeuyxx9S7d+8S20RHR2v27NnmtJeXl/nzf/7zHxUWFmrGjBlq3Lixdu/erUGDBikzM1NvvPGGpAvjLLp27aouXbpo+vTp+vnnn/XYY4+pRo0aGjx4sGM3ELhOMB4RqDi834Cyc2qQjYmJUUxMzGXbeHl5KSQkpMRl0dHRNkdoGzZsqH379un99983g+wnn3yivLw8ffjhh7Jarbr11luVmJiot95667JBNjc3V7m5ueZ0enq6PZsGXBe4rz1QcXi/Afar9GNkExISFBwcrCZNmmjYsGE6derUZdunpaUpMPB/F8XevHmzOnToIKvVas6LiorSvn37dObMmVL7mThxogICAsxHeHj4tW8M4GK4rz1QcXi/Afar1EE2Ojpa8+bN0+rVq/X6669r3bp1iomJUUFBQYntDxw4oHfffVdDhgwx56WkpKhOnTo27YqmU1JSSl336NGjlZaWZj6OHj1aDlsEuA7uaw9UHN5vwNWp1JffiouLM39u1qyZmjdvrkaNGikhIUGdO3e2afvbb78pOjpaf/7znzVo0LVfGNvLy8tmPC5Q1XBfe6Di8H4Drk6lPiJ7qYYNGyooKEgHDhywmX/8+HF16tRJd911lz744AObZSEhITpx4oTNvKLp0sbeAuC+9kBF4v0GXB2XCrLHjh3TqVOnFBoaas777bffdO+996pVq1aaPXu23NxsN6ldu3Zav3698vPzzXmrVq1SkyZNVLNmzQqrHXA13NceqDi834Cr49Qgm5GRocTERCUmJkqSDh06pMTERCUlJSkjI0Px8fH64YcfdPjwYa1evVo9e/ZU48aNFRUVJel/IbZu3bp64403dPLkSaWkpNiMfX344YdltVo1cOBA7dmzR59//rmmTZumESNGOGOTAZcSHztIT8UMkO8fX6K+Vm89FTOA+9oDDsD7DbCfxTCMS8eWV5iEhAR16tSp2Pz+/fvr/fffV69evbRjxw6dPXtWYWFh6tq1q8aPH2+erDVnzhz99a9/LbHvizdr165dGj58uLZu3aqgoCA98cQTGjlypF212nPfX+B6w3UtgYrD+w1VnT2Zy6lB1pUQZAEAABzPnszlUmNkAQAAgCIEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJdEkAXKSXZejo78fpx7ogNAKficRHnzcHYBgKsrKCzQlCUzNWP1fGXn5Zh36ImPHSR3N3dnlwcATsfnJByFIAtcoylLZmra8jkquiBzdl6Opi2fI0ka1XOo0+oCgMqCz0k4CkMLgGuQnZejGavn69K7ihiSPvjjyAMAVGV8TsKRCLLANUhNP13qh3DWH7eZBICqjM9JOBJBFrgGl7sXuq/VW8H+gRVcEQBULnxOwpEIssA1KDphwXLJfIukwZ3jSv3wBoCqgs9JOBInewHXKD52kKQLY72y8nLka/XW4D/OxgUA8DkJx7EYhnHp+GuUID09XQEBAUpLS5O/v7+zy0EllP3HWK/L/RsNAKoyPidRFvZkLo7IAuXEx+qtekFhzi4DACotPidR3hgjCwAAAJdEkAUAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIIsKlZ2XoyO/H1d2Xo6zSwEAoNzw/eYc3NkLFaKgsEBTlszUjNXzlZ2XIx+rt4b8cZ9tdzd3Z5cHAMBV4fvNuQiyqBBTlszUtOVzZPwxnZ2Xo2nL50iSRvUc6rS6AAC4Fny/ORdDC+Bw2Xk5mrF6vvkmL2JI+uCPv2ABAHA1fL85H0EWDpeafrrUN3NWXo5S009XcEUAAFw7vt+cjyALhwv2D5SP1bvEZb5WbwX7B1ZwRQAAXDu+35yPIAuHKxr4brlkvkXS4M5xpX4IAABQmfH95nyc7IUKER87SNKFMUNZeTnytXpr8B9ndQIA4Kr4fnMui2EYl45RRgnS09MVEBCgtLQ0+fv7O7scl5X9x5ihy/07BgAAV8P3W/mxJ3NxRBYVysfqrXpBYc4uAwCAcsX3m3MwRhYAAAAuyalBdv369YqNjVVYWJgsFosWLlxos3zAgAGyWCw2j+joaJs2p0+f1iOPPCJ/f3/VqFFDAwcOVEZGhk2bXbt26Z577pG3t7fCw8M1efJkR28aAAAAHMypQTYzM1MtWrTQe++9V2qb6OhoJScnm4/PPvvMZvkjjzyiPXv2aNWqVVq6dKnWr1+vwYMHm8vT09PVtWtX1atXT9u3b9eUKVM0duxYffDBBw7bLgAAADieU8fIxsTEKCYm5rJtvLy8FBISUuKyvXv3asWKFdq6datat24tSXr33Xd1//3364033lBYWJg++eQT5eXl6cMPP5TVatWtt96qxMREvfXWWzaB91K5ubnKzc01p9PT069iCwEAAOAolX6MbEJCgoKDg9WkSRMNGzZMp06dMpdt3rxZNWrUMEOsJHXp0kVubm768ccfzTYdOnSQ1Wo120RFRWnfvn06c+ZMqeudOHGiAgICzEd4eLgDtg4AAABXq1IH2ejoaM2bN0+rV6/W66+/rnXr1ikmJkYFBQWSpJSUFAUHB9s8x8PDQ4GBgUpJSTHb1KlTx6ZN0XRRm5KMHj1aaWlp5uPo0aPluWkAAAC4RpX68ltxcXHmz82aNVPz5s3VqFEjJSQkqHPnzg5dt5eXl7y8vBy6DgAAAFy9Sn1E9lINGzZUUFCQDhw4IEkKCQlRamqqTZvz58/r9OnT5rjakJAQnThxwqZN0XRpY28BAABQ+blUkD127JhOnTql0NBQSVK7du109uxZbd++3WyzZs0aFRYWqm3btmab9evXKz8/32yzatUqNWnSRDVr1qzYDQAAAEC5cWqQzcjIUGJiohITEyVJhw4dUmJiopKSkpSRkaH4+Hj98MMPOnz4sFavXq2ePXuqcePGioqKkiQ1bdpU0dHRGjRokLZs2aLvv/9ejz/+uOLi4hQWduHuGg8//LCsVqsGDhyoPXv26PPPP9e0adM0YsQIZ232FWXn5ejI78eVnZfj7FIAAEAVV5lzicUwDMNZK09ISFCnTp2Kze/fv7/ef/999erVSzt27NDZs2cVFhamrl27avz48TYnb50+fVqPP/64lixZIjc3Nz344IN65513VK1aNbPNrl27NHz4cG3dulVBQUF64oknNHLkSLtqtee+v1eroLBAU5bM1IzV85WdlyMfq7eGdI5TfOwgubu5O2SdAAAAJXFWLrEnczk1yLqSigiykxZN17Tlc3TxDrFIeipmgEb1HOqQdQIAAJTEWbnEnszlUmNkr2fZeTmasXq+Lv2rwpD0wR9/CQEAAFQEV8klBNlKIjX9dKm/FFl5OUpNP13BFQEAgKrKVXIJQbaSCPYPlI/Vu8RlvlZvBfsHVnBFAACgqnKVXEKQrSSKBlBbLplvkTS4c1ypv0wAAADlzVVySaW+s1dVEx87SNKFsSdZeTnytXpr8B9nBwIAAFQkV8glXLWgjCriqgVFsv8Ye3K5w/oAAAAVoaJziT2ZiyOylZCP1Vv1gsKcXQYAAEClziWMkQUAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkEWQAAALgkgiwAAABcEkEWAAAALokgCwAAAJdEkAUAAIBLIsgCAADAJRFkAQAA4JKcGmTXr1+v2NhYhYWFyWKxaOHChaW2HTp0qCwWi6ZOnWozf//+/erZs6eCgoLk7++v9u3ba+3atTZtkpKS1K1bN/n6+io4OFjx8fE6f/68A7YIAAAAFcWpQTYzM1MtWrTQe++9d9l2CxYs0A8//KCwsLBiy7p3767z589rzZo12r59u1q0aKHu3bsrJSVFklRQUKBu3bopLy9PmzZt0ty5czVnzhy99NJLDtkmAAAAVAyLYRiGs4uQJIvFogULFqhXr14283/77Te1bdtW3377rbp166ann35aTz/9tCTp999/V+3atbV+/Xrdc889kqRz587J399fq1atUpcuXbR8+XJ1795dx48fV506dSRJ06dP18iRI3Xy5ElZrdYS68nNzVVubq45nZ6ervDwcKWlpcnf37/8XwAAAAAoPT1dAQEBZcpclXqMbGFhofr27av4+HjdeuutxZbXqlVLTZo00bx585SZmanz589rxowZCg4OVqtWrSRJmzdvVrNmzcwQK0lRUVFKT0/Xnj17Sl33xIkTFRAQYD7Cw8PLfwMBAABw1Sp1kH399dfl4eGhJ598ssTlFotF3333nXbs2KHq1avL29tbb731llasWKGaNWtKklJSUmxCrCRzumj4QUlGjx6ttLQ083H06NFy2ioAAACUBw9nF1Ca7du3a9q0afrpp59ksVhKbGMYhoYPH67g4GBt2LBBPj4+mjVrlmJjY7V161aFhoZe9fq9vLzk5eV11c8HAACAY1XaI7IbNmxQamqq6tatKw8PD3l4eOjIkSN69tlnVb9+fUnSmjVrtHTpUs2fP1933323br/9dv3zn/+Uj4+P5s6dK0kKCQnRiRMnbPoumg4JCanQbQIAAED5qbRBtm/fvtq1a5cSExPNR1hYmOLj4/Xtt99KkrKysiRJbm62m+Hm5qbCwkJJUrt27fTzzz8rNTXVXL5q1Sr5+/srIiKigrYGAAAA5c2pQwsyMjJ04MABc/rQoUNKTExUYGCg6tatq1q1atm09/T0VEhIiJo0aSLpQkitWbOm+vfvr5deekk+Pj6aOXOmDh06pG7dukmSunbtqoiICPXt21eTJ09WSkqKxowZo+HDhzN0AAAAwIU59Yjstm3bFBkZqcjISEnSiBEjFBkZWeZrvAYFBWnFihXKyMjQfffdp9atW2vjxo1atGiRWrRoIUlyd3fX0qVL5e7urnbt2unRRx9Vv3799MorrzhsuwAAAOB4leY6spWdPdc0AwAAwNW5bq4jCwAAAJSGIAsAAACXRJAFAACASyLIAgAAwCURZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkezi7AVRiGIUlKT093ciUAAADXr6KsVZS9LocgW0bnzp2TJIWHhzu5EgAAgOvfuXPnFBAQcNk2FqMscRcqLCzU8ePHVb16dVksFmeX47LS09MVHh6uo0ePyt/f39nloIzYb66J/eaa2G+uif1WfgzD0Llz5xQWFiY3t8uPguWIbBm5ubnpxhtvdHYZ1w1/f3/e6C6I/eaa2G+uif3mmthv5eNKR2KLcLIXAAAAXBJBFgAAAC6JIIsK5eXlpZdfflleXl7OLgV2YL+5Jvaba2K/uSb2m3NwshcAAABcEkdkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFtfsvffeU/369eXt7a22bdtqy5YtZXre/PnzZbFY1KtXL5v5GRkZevzxx3XjjTfKx8dHERERmj59ugMqr9rKe7+dOHFCAwYMUFhYmHx9fRUdHa1ff/3VAZVXbfbstzlz5shisdg8vL29bdoYhqGXXnpJoaGh8vHxUZcuXdhvDlDe++3rr79W165dVatWLVksFiUmJjp4C6qm8txv+fn5GjlypJo1ayY/Pz+FhYWpX79+On78eEVsynWLIItr8vnnn2vEiBF6+eWX9dNPP6lFixaKiopSamrqZZ93+PBhPffcc7rnnnuKLRsxYoRWrFihjz/+WHv37tXTTz+txx9/XIsXL3bUZlQ55b3fDMNQr1699N///leLFi3Sjh07VK9ePXXp0kWZmZmO3JQq5Wr2m7+/v5KTk83HkSNHbJZPnjxZ77zzjqZPn64ff/xRfn5+ioqKUk5OjqM3p8pwxH7LzMxU+/bt9frrrzu6/CqrvPdbVlaWfvrpJ7344ov66aef9PXXX2vfvn3q0aNHRWzO9csArsEdd9xhDB8+3JwuKCgwwsLCjIkTJ5b6nPPnzxt33XWXMWvWLKN///5Gz549bZbfeuutxiuvvGIz7/bbbzdeeOGFcq29Kivv/bZv3z5DkrF7926bPmvXrm3MnDnTIdtQFdm732bPnm0EBASU2l9hYaEREhJiTJkyxZx39uxZw8vLy/jss8/Kre6qrrz328UOHTpkSDJ27NhRDpXiYo7cb0W2bNliSDKOHDlyLaVWaRyRxVXLy8vT9u3b1aVLF3Oem5ubunTpos2bN5f6vFdeeUXBwcEaOHBgicvvuusuLV68WL/99psMw9DatWu1f/9+de3atdy3oSpyxH7Lzc2VJJt/o7m5ucnLy0sbN24sx+qrrqvdbxkZGapXr57Cw8PVs2dP7dmzx1x26NAhpaSk2PQZEBCgtm3bXrZPlJ0j9hscr6L2W1pamiwWi2rUqFFepVc5BFlctd9//10FBQWqU6eOzfw6deooJSWlxOds3LhR//rXvzRz5sxS+3333XcVERGhG2+8UVarVdHR0XrvvffUoUOHcq2/qnLEfrvllltUt25djR49WmfOnFFeXp5ef/11HTt2TMnJyeW+DVXR1ey3Jk2a6MMPP9SiRYv08ccfq7CwUHfddZeOHTsmSebz7OkT9nHEfoPjVcR+y8nJ0ciRI9WnTx/5+/uX+zZUFR7OLgBVx7lz59S3b1/NnDlTQUFBpbZ799139cMPP2jx4sWqV6+e1q9fr+HDhyssLMzmr2NUjLLsN09PT3399dcaOHCgAgMD5e7uri5duigmJkYGNw90mnbt2qldu3bm9F133aWmTZtqxowZGj9+vBMrw+Ww31yTPfstPz9ff/nLX2QYht5///2KLvW6QpDFVQsKCpK7u7tOnDhhM//EiRMKCQkp1v7gwYM6fPiwYmNjzXmFhYWSJA8PD+3bt09hYWH6+9//rgULFqhbt26SpObNmysxMVFvvPEGQbYcOGK/NWrUSK1atVJiYqLS0tKUl5en2rVrq23btmrdurVjN6iKsHe/lcTT01ORkZE6cOCAJJnPO3HihEJDQ236bNmyZfkUXsU5Yr/B8Ry534pC7JEjR7RmzRqOxl4jhhbgqlmtVrVq1UqrV6825xUWFmr16tU2f5UWueWWW/Tzzz8rMTHRfPTo0UOdOnVSYmKiwsPDlZ+fr/z8fLm52f5quru7m+EJ18YR++1iAQEBql27tn799Vdt27ZNPXv2dPg2VQX27reSFBQU6OeffzZDa4MGDRQSEmLTZ3p6un788ccy94nLc8R+g+M5ar8Vhdhff/1V3333nWrVqlXutVc5Tj7ZDC5u/vz5hpeXlzFnzhzjl19+MQYPHmzUqFHDSElJMQzDMPr27WuMGjWq1OeXdNWCjh07Grfeequxdu1a47///a8xe/Zsw9vb2/jnP//pyE2pUhyx3/79738ba9euNQ4ePGgsXLjQqFevntG7d29HbkaVY+9+GzdunPHtt98aBw8eNLZv327ExcUZ3t7exp49e8w2kyZNMmrUqGEsWrTI2LVrl9GzZ0+jQYMGRnZ2doVv3/XKEfvt1KlTxo4dO4xly5YZkoz58+cbO3bsMJKTkyt8+65X5b3f8vLyjB49ehg33nijkZiYaCQnJ5uP3Nxcp2zj9YChBbgmDz30kE6ePKmXXnpJKSkpatmypVasWGEOkE9KSip2dPVK5s+fr9GjR+uRRx7R6dOnVa9ePU2YMEFDhw51xCZUSY7Yb8nJyRoxYoT5b+p+/frpxRdfdET5VZa9++3MmTMaNGiQUlJSVLNmTbVq1UqbNm1SRESE2eb5559XZmamBg8erLNnz6p9+/ZasWJFsQvw4+o5Yr8tXrxYf/3rX83puLg4SdLLL7+ssWPHVsyGXefKe7/99ttv5vXQLx26s3btWt17770Vsl3XG4thcCYGAAAAXA9jZAEAAOCSCLIAAABwSQRZAAAAuCSCLAAAAFwSQRYAAAAuiSALAAAAl0SQBQAAgEsiyAIAAMAlEWQBAADgkgiyAAAAcEkEWQCV2r333qunn3660vaHq/fcc8+pV69edj3n1KlTCg4O1uHDhx1Skz3i4uL05ptvOrsMoEojyAJwmgEDBshisZiPWrVqKTo6Wrt27XJ2aVVeRQT+xMREtWzZ8rJt/vrXv2rMmDHm9IQJE9SzZ0/Vr19fkjRx4kS1adNG1atXV3BwsHr16qV9+/ZddU2Xru9yxowZowkTJigtLe2q1wfg2hBkAThVdHS0kpOTlZycrNWrV8vDw0Pdu3d3dlkoJ3l5eaUu27lz52WDbEFBgZYuXaoePXpIkrKysvSvf/1LAwcONNusW7dOw4cP1w8//KBVq1YpPz9fXbt2VWZmpt21Xrq+K7ntttvUqFEjffzxx3avC0D5IMgCcCovLy+FhIQoJCRELVu21KhRo3T06FGdPHmyxPa5ubl68sknFRwcLG9vb7Vv315bt241lxcWFmry5Mlq3LixvLy8VLduXU2YMMGmj8LCQj3//PMKDAxUSEiIxo4dW2p9S5cuVY0aNVRQUCDpwlFEi8WiUaNGmW3+9re/6dFHHzWnV6xYofbt26tGjRqqVauWunfvroMHD0qSPvjgA4WFhamwsNBmPT179tRjjz1m1jdx4kQ1aNBAPj4+atGihb788kuzbXn0cbnXasCAAVq3bp2mTZtmHi0v+lf+lV7/e++9V48//riefvppBQUFKSoqqsTX9dixY/r999/NIHv27FnFxsaqffv2SklJkSRt2rRJnp6eatOmjSTpm2++kZeXl+68806b13rAgAG69dZb1aJFC82ZM0dJSUnavn272aZp06Y2R/4vfvzjH/8w2126vpdfflnNmjWTn5+f6tSpo2HDhik/P99mO2JjYzV//vwStxFABTAAwEn69+9v9OzZ05w+d+6cMWTIEKNx48ZGQUGBYRiG0bFjR+Opp54y2zz55JNGWFiY8c033xh79uwx+vfvb9SsWdM4deqUYRiG8fzzzxs1a9Y05syZYxw4cMDYsGGDMXPmTPP5HTt2NPz9/Y2xY8ca+/fvN+bOnWtYLBZj5cqVJdZ49uxZw83Nzdi6dathGIYxdepUIygoyGjbtq3ZpnHjxjbr+PLLL42vvvrK+PXXX40dO3YYsbGxRrNmzYyCggLj9OnThtVqNb777juz/alTp2zmvfrqq8Ytt9xirFixwjh48KAxe/Zsw8vLy0hISDAMwyiXPi73Wp09e9Zo166dMWjQICM5OdlITk42zp8/X6bXv2PHjka1atWM+Ph44z//+Y/xn//8p8TXdcmSJUaNGjUMwzCMXbt2GY0bNzaGDBli5OXlmW2ee+45Y/DgwTb7Pjo6usT+ivz666+GJOPnn3825+3Zs8eQZKxevdpITk42Dh8+bLi5uRlffPGFkZOTU+L6CgsLjRdffNH4/vvvjcOHDxvffPONUbt2beOf//ynzfqWL19uWK1Wm34AVByCLACn6d+/v+Hu7m74+fkZfn5+hiQjNDTU2L59u9nm4iCbkZFheHp6Gp988om5PC8vzwgLCzMmT55spKenG15eXjah8lIdO3Y02rdvbzOvTZs2xsiRI0t9zu23325MmTLFMAzD6NWrlzFhwgTDarUa586dM44dO2ZIMvbv31/q80+ePGkTrnr27Gk89thj5vIZM2YYYWFhRkFBgZGTk2P4+voamzZtsulj4MCBRp8+fczpa+3jSq/VpX9AGMaVX/+i50VGRpb6WhQZP3680bFjR+OTTz4xatasaXzwwQfF2tx0003G0qVLS93mSxUUFBjdunUz7r77bpv5a9asMTw8PMywuW3bNkOSkZKSctn1XapPnz7FXpOdO3cakozDhw+X+jwAjsPQAgBO1alTJyUmJioxMVFbtmxRVFSUYmJidOTIkWJtDx48qPz8fN19993mPE9PT91xxx3au3ev9u7dq9zcXHXu3Pmy62zevLnNdGhoqFJTU0tt37FjRyUkJMgwDG3YsEG9e/dW06ZNtXHjRq1bt05hYWG66aabzPa//vqr+vTpo4YNG8rf3988MSkpKUmS9Mgjj+irr75Sbm6uJOmTTz5RXFyc3NzcdODAAWVlZelPf/qTqlWrZj7mzZtnDk8ojz7K+lpd7Eqvf5FWrVpdsa/ExETt2rVLjz/+uJYtW6ZBgwbZLN+7d6+OHz9uU192dra8vb1L7XP48OHavXt3sX/1//zzz7r55pvl5eUl6cLY3ODgYNWpU6fU9R05ckTDhw/Xbbfdppo1a6patWr697//rRtvvNGmbx8fH0kXxu8CqHgezi4AQNXm5+enxo0bm9OzZs1SQECAZs6cqVdffdWuvopCxZV4enraTFsslmLjTS9277336sMPP9TOnTvl6empW265Rffee68SEhJ05swZdezY0aZ9bGys6tWrp5kzZ5pjWW+77TbzxKfY2FgZhqFly5apTZs22rBhg95++21JUkZGhiRp2bJluuGGG2z6LQpi5dFHWV+rq+Hn53fFNomJierdu7c+/fRTnT17ttjyxYsX609/+pNNcA0KCtKZM2dK7O/xxx/X0qVLtX79+mJhc9euXWrWrJk5vXPnTpvpS9d38uRJtWnTRvfdd5/eeust3XDDDSooKFDr1q3VokULm+edPn1aklS7du0rbjOA8scRWQCVisVikZubm7Kzs4sta9SokaxWq77//ntzXn5+vrZu3aqIiAjddNNN8vHx0erVq8u1pnvuuUfnzp3T22+/bYbWoiCbkJCge++912x76tQp7du3T2PGjFHnzp3VtGnTYuHL29tbvXv31ieffKLPPvtMTZo00e233y5JioiIkJeXl5KSktS4cWObR3h4eLn1caXXymq1mie4FbnS619W586d03//+18NHz5c//jHPxQXF6c9e/bYtFm0aJF69uxpMy8yMlK//PKLzTzDMPT4449rwYIFWrNmjRo0aFBsfbt27bI5Cr9z585iR+UvXt+SJUtUUFCgzz77TF27dtWtt96q9evXKz8/v9hVFnbv3q0bb7xRQUFBZd5+AOWHI7IAnCo3N9c8S/3MmTP6xz/+oYyMDMXGxhZr6+fnp2HDhik+Pl6BgYGqW7euJk+erKysLA0cOFDe3t4aOXKknn/+eVmtVt199906efKk9uzZY3PJJnvVrFlTzZs31yeffGKe5d6hQwf95S9/UX5+vs0R2Zo1a6pWrVr64IMPFBoaqqSkJJsrHBR55JFH1L17d+3Zs8fmigfVq1fXc889p2eeeUaFhYVq37690tLS9P3338vf31/9+/cvlz6u9FrVr19fP/74ow4fPqxq1aopMDDwiq9/We3cuVPu7u6KiIhQZGSkdu/erdjYWG3ZskVBQUFKTU3Vtm3btHjxYpvnRUVFafTo0Tpz5oxq1qwp6cJwgk8//VSLFi1S9erVzd+lgIAA+fj4qLCwUHv27NFLL71k9nPw4EH17t3bnL50fbVq1VJ6eroWL16siIgILVmyRBMnTtQNN9xQ7Mjrhg0b1LVr1zJvO4By5twhugCqsv79+xuSzEf16tWNNm3aGF9++aXZ5tKTjrKzs40nnnjCCAoKMry8vIy7777b2LJli7m8oKDAePXVV4169eoZnp6eRt26dY3XXnut1P4M48JJRP37979srU899ZQhydi7d685r0WLFkZISEixtqtWrTKaNm1qeHl5Gc2bNzcSEhIMScaCBQts6gwNDTUkGQcPHrR5fmFhoTF16lSjSZMmhqenp1G7dm0jKirKWLdunU27a+3jcq/Vvn37jDvvvNPw8fExJBmHDh0yDOPKr39Jr++l3n33XeO2224zp8+fP29ER0cb99xzj5Gbm2vMmjWr2AlbRe644w5j+vTp5vTFvz8XP2bPnm0YhmHs37/fkGQcOXLEfE5MTIxRo0YNY+PGjYZhGMXWV1BQYAwZMsSoXr26ERwcbIwYMcL4f//v/xndunWzqSU7O9sICAgwNm/efNntBeA4FsMwDCfkZwAAStSjRw+1b99ezz//fLFly5YtU3x8vHbv3i03t/IZHXe59V3O+++/rwULFmjlypXlUgcA+zG0AABQqbRv3159+vQpcVm3bt3066+/6rfffrMZM+yo9V2Op6en3n333XKpAcDV4YgsAAAAXBJXLQAAAIBLIsgCAADAJRFkAQAA4JIIsgAAAHBJBFkAAAC4JIIsAAAAXBJBFgAAAC6JIAsAAACXRJAFAACAS/r/QeDl0rFBKOcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resonance_finder = ResonanceFinder(freq_window=(freq0 - fwidth, freq0 + fwidth))\n",
    "zone_edge_freqs = None\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4.8), tight_layout=True)\n",
    "\n",
    "for i, bloch_k in enumerate(k_values):\n",
    "    resonance_data = resonance_finder.run(signals=band_results[f\"sim_{i}\"].data)\n",
    "    resonance_data = resonance_data.where(abs(resonance_data.Q) > 0, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.amplitude > 1, drop=True)\n",
    "    resonance_data = resonance_data.where(resonance_data.error < 100, drop=True)\n",
    "\n",
    "    frequencies = resonance_data.freq.to_numpy()\n",
    "    wavelengths_nm = 1000 * td.C_0 / frequencies\n",
    "\n",
    "    ax.scatter(np.full(len(wavelengths_nm), bloch_k), wavelengths_nm, s=18, color=\"C0\")\n",
    "\n",
    "    if np.isclose(bloch_k, 0.5):\n",
    "        zone_edge_freqs = frequencies\n",
    "\n",
    "band_edges = zone_edge_freqs[np.argsort(abs(zone_edge_freqs - freq0))[:2]]\n",
    "bandgap_center_nm = 1000 * td.C_0 / np.mean(band_edges)\n",
    "\n",
    "ax.axhline(bandgap_center_nm, linestyle=\"--\", label=f\"Bandgap center: {bandgap_center_nm:.0f} nm\")\n",
    "\n",
    "ax.set_xlabel(r\"Bloch wavevector $k/(2\\pi/a)$\")\n",
    "ax.set_ylabel(\"Wavelength (nm)\")\n",
    "ax.set_ylim(1470, 1650)\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea052ffb",
   "metadata": {},
   "source": [
    "## Input waveguide design from the Bragg condition\n",
    "\n",
    "The PhC reflector is connected to an uncorrugated access waveguide through an apodized transition to reduce mode mismatch between the guided mode and the Bloch mode of the strongly corrugated PhC. The initial access-waveguide width $W_1$ is chosen using the first-order Bragg condition\n",
    "\n",
    "$$\n",
    "2n_{\\mathrm{eff}}a=\\lambda_{\\mathrm{B}},\n",
    "$$\n",
    "\n",
    "where $n_{\\mathrm{eff}}$ is the effective index of the fundamental mode of the uncorrugated waveguide, $a$ is the PhC period, and $\\lambda_{\\mathrm{B}}$ is the target Bragg wavelength.\n",
    "\n",
    "The calculation below sweeps $W_1$, evaluates $n_{\\mathrm{eff}}$, and converts it to the corresponding Bragg period. The width matching the chosen period gives the input-waveguide design point. Note that $W_1$ is the width of the uncorrugated access waveguide and is distinct from the narrow PhC backbone width $W_2$. The full photonic bandgap is determined from the periodic unit-cell calculation above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc065b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "waveguide_widths_um = np.linspace(0.40, 1.20, 17)\n",
    "mode_solvers = {}\n",
    "\n",
    "for waveguide_width_um in waveguide_widths_um:\n",
    "    task_name = f\"width={waveguide_width_um:.3f}\"\n",
    "    cross_section = waveguide.RectangularDielectric(\n",
    "        wavelength=target_wavelength_um, core_width=waveguide_width_um,\n",
    "        core_thickness=film_thickness_um, slab_thickness=slab_thickness_um,\n",
    "        sidewall_angle=sidewall_angle, core_medium=ln,\n",
    "        clad_medium=air, box_medium=silica,\n",
    "        mode_spec=td.ModeSpec(num_modes=1, num_pml=(12, 12), precision=\"double\"))\n",
    "    mode_solvers[task_name] = cross_section.mode_solver\n",
    "\n",
    "cross_section.plot_structures(x=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43f68c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "731b86f7a48542759354e56929fd06b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:44:02 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:44:02 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m17\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:44:17 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.066</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:44:17 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.066\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab5320075c74473c9dc043217c6d1e96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:44:50 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:44:50 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_batch = web.Batch(simulations=mode_solvers, verbose=True)\n",
    "mode_results = mode_batch.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50e9170e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waveguide width for a = 445 nm: 606.1 nm\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widths_um = []\n",
    "periods_um = []\n",
    "\n",
    "for task_name, mode_data in mode_results.items():\n",
    "    waveguide_width_um = float(task_name.split(\"=\")[-1])\n",
    "    effective_index = np.real(mode_data.n_eff.isel(mode_index=0, f=0).item())\n",
    "    widths_um.append(waveguide_width_um)\n",
    "    periods_um.append(target_wavelength_um / (2 * effective_index))\n",
    "\n",
    "order = np.argsort(widths_um)\n",
    "widths_um = np.asarray(widths_um)[order]\n",
    "periods_um = np.asarray(periods_um)[order]\n",
    "\n",
    "selected_width_um = np.interp(period_um, periods_um[::-1], widths_um[::-1])\n",
    "print(f\"Waveguide width for a = {1000 * period_um:.0f} nm: {1000 * selected_width_um:.1f} nm\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4.8), tight_layout=True)\n",
    "ax.plot(1000 * widths_um, 1000 * periods_um, \"o-\")\n",
    "\n",
    "ax.set_xlabel(r\"Input waveguide width $W_1$ (nm)\")\n",
    "ax.set_ylabel(r\"PhC period $a$ (nm)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de65423",
   "metadata": {},
   "source": [
    "## Apodized Bragg reflector\n",
    "\n",
    "An abrupt transition from the access waveguide to the strongly corrugated PhC can cause mode mismatch between the propagating guided mode and the PhC Bloch mode. To reduce this mismatch, both the central backbone width and the corrugation width are varied smoothly through an apodized transition.\n",
    "\n",
    "\n",
    "The finite reflector consists of a uniform PhC section between two symmetric apodized transitions. We compare the Bragg-matched input width obtained above with a deliberately mismatched wider waveguide and evaluate their reflection, transmission, and guided-mode leakage using 3D FDTD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b524788c",
   "metadata": {},
   "outputs": [],
   "source": [
    "taper_cells = 35\n",
    "main_cells = 5\n",
    "\n",
    "def make_reflector_simulation(input_width, freqs):\n",
    "    taper_length = taper_cells * period_um\n",
    "    main_length = main_cells * period_um\n",
    "    device_length = 2 * taper_length + main_length\n",
    "\n",
    "    def apodization_ratio(x):\n",
    "        if x < 0 or x > device_length:\n",
    "            return 0\n",
    "        if x <= taper_length:\n",
    "            return (np.sin((x / taper_length - 0.5) * np.pi) + 1) / 2\n",
    "        if x <= taper_length + main_length:\n",
    "            return 1\n",
    "        return (np.sin(((device_length - x) / taper_length - 0.5) * np.pi) + 1) / 2\n",
    "\n",
    "    x_left = np.linspace(0, taper_length, 200)\n",
    "    x_main = np.linspace(taper_length, taper_length + main_length, 50)\n",
    "    x_right = np.linspace(taper_length + main_length, device_length, 200)\n",
    "    x_backbone = np.concatenate(([-100, 0], x_left[1:], x_main[1:], x_right[1:], [device_length + 100]))\n",
    "\n",
    "    y_backbone = np.array([\n",
    "        (input_width + (backbone_width_um - input_width) * apodization_ratio(x)) / 2\n",
    "        for x in x_backbone\n",
    "    ])\n",
    "    backbone_vertices = np.vstack((\n",
    "        np.column_stack((x_backbone, y_backbone)),\n",
    "        np.column_stack((x_backbone[::-1], -y_backbone[::-1])),\n",
    "    ))\n",
    "\n",
    "    structures = [\n",
    "        td.Structure(\n",
    "            geometry=td.PolySlab(\n",
    "                vertices=backbone_vertices, axis=2,\n",
    "                slab_bounds=(-etch_depth_um / 2, etch_depth_um / 2),\n",
    "                sidewall_angle=sidewall_angle, reference_plane=\"top\",\n",
    "            ),\n",
    "            medium=ln,\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    duty_start = 0.5 - duty_cycle / 2\n",
    "    duty_end = 0.5 + duty_cycle / 2\n",
    "    for i in range(2 * taper_cells + main_cells):\n",
    "        tooth_start = (i + duty_start) * period_um\n",
    "        tooth_end = (i + duty_end) * period_um\n",
    "        tooth_center = (tooth_start + tooth_end) / 2\n",
    "        ratio = apodization_ratio(tooth_center)\n",
    "\n",
    "        local_backbone_width = input_width + (backbone_width_um - input_width) * ratio\n",
    "        local_corrugation_width = corrugation_width_um * ratio\n",
    "        y_inner = local_backbone_width / 2\n",
    "        y_outer = y_inner + local_corrugation_width\n",
    "\n",
    "        for vertices in [\n",
    "            [(tooth_start, y_inner), (tooth_end, y_inner), (tooth_end, y_outer), (tooth_start, y_outer)],\n",
    "            [(tooth_start, -y_inner), (tooth_end, -y_inner), (tooth_end, -y_outer), (tooth_start, -y_outer)],\n",
    "        ]:\n",
    "            structures.append(\n",
    "                td.Structure(\n",
    "                    geometry=td.PolySlab(\n",
    "                        vertices=vertices, axis=2,\n",
    "                        slab_bounds=(-etch_depth_um / 2, etch_depth_um / 2),\n",
    "                        sidewall_angle=sidewall_angle, reference_plane=\"top\",\n",
    "                    ),\n",
    "                    medium=ln,\n",
    "                )\n",
    "            )\n",
    "\n",
    "    structures.extend([\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(-100, -100, -etch_depth_um / 2 - slab_thickness_um),\n",
    "                rmax=(100, 100, -etch_depth_um / 2),\n",
    "            ),\n",
    "            medium=ln,\n",
    "        ),\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(-100, -100, -100),\n",
    "                rmax=(100, 100, -etch_depth_um / 2 - slab_thickness_um),\n",
    "            ),\n",
    "            medium=silica,\n",
    "        ),\n",
    "    ])\n",
    "\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(-2, 0, 0), size=(0, 3, 3), mode_index=0, direction=\"+\",\n",
    "        source_time=td.GaussianPulse(\n",
    "            freq0=(np.min(freqs) + np.max(freqs)) / 2,\n",
    "            fwidth=(np.max(freqs) - np.min(freqs)) / 3,\n",
    "        ),\n",
    "        mode_spec=td.ModeSpec(num_modes=1), num_freqs=7,\n",
    "    )\n",
    "\n",
    "    monitors = [\n",
    "        td.ModeMonitor(\n",
    "            center=(-1, 0, 0), size=(0, 3, 3), freqs=freqs,\n",
    "            mode_spec=td.ModeSpec(num_modes=1), name=\"mode_refl\",\n",
    "        ),\n",
    "        td.ModeMonitor(\n",
    "            center=(device_length + 1, 0, 0), size=(0, 3, 3), freqs=freqs,\n",
    "            mode_spec=td.ModeSpec(num_modes=1), name=\"mode_trans\",\n",
    "        ),\n",
    "    ]\n",
    "\n",
    "    sim_x_min = -5\n",
    "    sim_x_max = device_length + 5\n",
    "    simulation = td.Simulation(\n",
    "        center=((sim_x_min + sim_x_max) / 2, 0, 0),\n",
    "        size=(sim_x_max - sim_x_min, 5, film_thickness_um + 3),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=20),\n",
    "        structures=structures, sources=[mode_source], monitors=monitors,\n",
    "        run_time=10e-12,\n",
    "        boundary_spec=td.BoundarySpec(\n",
    "            x=td.Boundary.absorber(), y=td.Boundary.absorber(), z=td.Boundary.absorber()\n",
    "        ),\n",
    "    )\n",
    "    return simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05825135",
   "metadata": {},
   "source": [
    "The Bragg-matched input width obtained above is compared with a deliberately mismatched wider waveguide while keeping the PhC geometry unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5fe97f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reflector_freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 400)\n",
    "\n",
    "reflector_simulations = {}\n",
    "for label, input_width in {\"matched\": selected_width_um, \"mismatched\": 1.0}.items():\n",
    "    reflector_simulations[label] = make_reflector_simulation(input_width, reflector_freqs)\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(12, 6), tight_layout=True)\n",
    "for ax, (label, simulation) in zip(axes, reflector_simulations.items()):\n",
    "    simulation.plot(z=0, ax=ax)\n",
    "    ax.set_title(label.capitalize())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8e1995d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "069041072ee641fbaa6ee81862667d81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:45:00 EDT </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:45:00 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:45:07 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.298</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:45:07 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m12.298\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after    \n",
       "\u001b[2;36m             \u001b[0mcompletion.                                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3da0438338445f49dc3161520b58de0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:46:29 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m02:46:29 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reflector_batch = web.Batch(simulations=reflector_simulations, verbose=True)\n",
    "reflector_results = reflector_batch.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9128a0",
   "metadata": {},
   "source": [
    "### Reflectance and guided-mode leakage\n",
    "\n",
    "If the optical power remains in the monitored fundamental waveguide mode, $1-(R+T)$ stays close to unity. Since the material models are lossless, a reduction in $1-(R+T)$ primarily indicates coupling to radiation, slab, or higher-order modes.\n",
    "\n",
    "\n",
    "The matched reflectance is plotted together with $1-(R+T)$ for both input-waveguide designs. This distinguishes reduced reflection caused by transmission from power coupled out of the monitored fundamental modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e26290a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 4.8), tight_layout=True)\n",
    "\n",
    "for label, color in zip([\"matched\", \"mismatched\"], [\"C0\", \"C1\"]):\n",
    "    refl_amps = reflector_results[label][\"mode_refl\"].amps.sel(direction=\"-\", mode_index=0)\n",
    "    trans_amps = reflector_results[label][\"mode_trans\"].amps.sel(direction=\"+\", mode_index=0)\n",
    "\n",
    "    wavelengths_nm = 1000 * td.C_0 / np.asarray(trans_amps.f.values)\n",
    "    reflectance = np.abs(np.asarray(refl_amps.values).squeeze()) ** 2\n",
    "    transmittance = np.abs(np.asarray(trans_amps.values).squeeze()) ** 2\n",
    "    order = np.argsort(wavelengths_nm)\n",
    "\n",
    "    r_db = 10 * np.log10(np.clip(reflectance, 1e-8, None))\n",
    "    rt_db = 10 * np.log10(np.clip(reflectance + transmittance, 1e-8, None))\n",
    "\n",
    "    if label == \"matched\":\n",
    "        ax.plot(wavelengths_nm[order], r_db[order], color=color, label=\"Matched $R$\")\n",
    "\n",
    "    ax.plot(wavelengths_nm[order], rt_db[order], \"--\", color=color, label=f\"{label.capitalize()} $R+T$\")\n",
    "\n",
    "ax.set_xlabel(\"Wavelength (nm)\")\n",
    "ax.set_ylabel(\"Guided-mode power (dB)\")\n",
    "ax.set_xlim(1520, 1600)\n",
    "ax.set_ylim(-2, 0)\n",
    "ax.legend(frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf45bab",
   "metadata": {},
   "source": [
    "### Short-wavelength loss\n",
    "\n",
    "The mismatched input waveguide shows a larger reduction in $R+T$ toward the short-wavelength side of the reflection band, indicating increased coupling out of the monitored fundamental mode. A similar behavior was reported by Hwang et al., where the additional short-wavelength loss of a non-optimized transition was attributed to excitation of a leaky slab mode.\n",
    "\n",
    "\n",
    "The $R+T$ spectrum identifies the presence of modal leakage but does not uniquely determine the destination of the missing power. Field profiles or additional modal analysis would be required to distinguish radiation, slab-mode excitation, and higher-order mode conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d3b1ef",
   "metadata": {},
   "source": [
    "\n",
    "## Effect of LN film thickness\n",
    "\n",
    "The present notebook considers a fixed LN film thickness, but the same workflow can be extended to study its influence on the reflector design. In general, a thinner LN film can produce a wider photonic bandgap, whereas a thicker film tends to provide stronger vertical confinement but a narrower bandgap and greater involvement of higher-order modes.\n",
    "\n",
    "For each film thickness, the unit-cell band structure can be recalculated, followed by the Bragg-condition analysis to determine the corresponding input-waveguide width. The finite-reflector FDTD simulation can then be repeated to compare the reflection bandwidth and guided-mode leakage."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Photonic crystals",
   "Passive photonic integrated circuit components",
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "Photonic crystal Bragg reflectors act as compact mirrors for Fabry-Perot microresonators while preserving the electro-optic and nonlinear properties of thin-film lithium niobate (TFLN). This notebook uses Tidy3D to design a fishbone-shaped TFLN photonic crystal reflector: we compute the Bloch-mode band structure of the periodic unit cell with the resonance finder, link the access-waveguide effective index to the Bragg period with a mode solver sweep, and evaluate the reflectance and guided-mode leakage of a finite apodized reflector in 3D FDTD.",
  "feature_image": "./img/TFLNFabryPerot.png",
  "features": [
   "Mode analysis",
   "Resonance finder",
   "Parameter sweep"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "thin-film lithium niobate, TFLN, LiNbO3, photonic crystal, Bragg reflector, Fabry-Perot, microresonator, band structure, photonic bandgap, apodization, Bloch mode, resonance finder, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  },
  "title": "How to model TFLN photonic crystal Bragg reflectors in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}