{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80809059",
   "metadata": {},
   "source": [
    "# Thin film lithium niobate adiabatic waveguide coupler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15720897",
   "metadata": {},
   "source": [
    "> **Note: the cost of running the entire notebook is over 20 FlexCredits.**\n",
    "\n",
    "Thin-film lithium niobate (LN) offers several advantages over silicon in integrated photonics. LN's inherent electro-optic and nonlinear properties exceed those of silicon, allowing for efficient electro-optic modulation and wavelength conversion, which are vital for optical signal processing. Furthermore, LN demonstrates lower optical loss in the telecom wavelengths, and it supports operation in a broader wavelength range compared to silicon, extending into the mid-infrared region. Thus, thin-film LN's characteristics present opportunities for the realization of high-performance, multi-functional integrated photonic devices.\n",
    "\n",
    "In this notebook, we introduce an adiabatic LN waveguide coupler based on a special arrayed scheme with three waveguides. The coupling mechanism is analogous to the stimulated Raman adiabatic passage (STIRAP) in quantum physics. Adiabatic light transfer can be achieved from one outer waveguide to the other outer waveguide with near unity coupling efficiency and minimum excitation of the middle waveguide. More importantly, the designed coupler has a high fabrication tolerance, small mode and polarization sensitivity, and large working bandwidth. The design is based on `Yi-Xin Lin, Mohammadreza Younesi, Hung-Pin Chung, Hua-Kung Chiu, Reinhard Geiss, Quan-Hsiang Tseng, Frank Setzpfandt, Thomas Pertsch, and Yen-Hung Chen, \"Ultra-compact, broadband adiabatic passage optical couplers in thin-film lithium niobate on insulator waveguides,\" Opt. Express 29, 27362-27372 (2021)` [DOI: 10.1364/OE.435633](https://doi.org/10.1364/OE.435633).\n",
    "\n",
    "Adiabatic devices usually have a better fabrication tolerance and working bandwidth. However, the device size is inevitably larger, making rigorous simulation a daunting task. Photonic designers often resort to approximation methods such as 2D FDTD, beam propagation method, eigenmode expansion, and so on. The accuracy of these simulations is often questionable. `Tidy3D` allows photonic designers to perform accurate 3D FDTD for large devices in a short amount of time, eliminating all the compromises and giving you confidence in your design. \n",
    "\n",
    "<img src=\"img/LN_adiabatic_coupler.png\" width=\"400\" alt=\"Schematic of the adiabatic coupler\">\n",
    "\n",
    "For more integrated photonic examples such as the [8-Channel mode and polarization de-multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/8ChannelDemultiplexer/), the [broadband bi-level taper polarization rotator-splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/BilevelPSR/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e7fb6da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdstk\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a036303",
   "metadata": {},
   "source": [
    "## Simulation Setup "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b89160",
   "metadata": {},
   "source": [
    "First, we use the convenience class [FreqRange](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FreqRange.html) to define the basic simulation parameters related to frequency and wavelength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "793d5548",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "wvl_width = 0.2  # one-sided wavelength range\n",
    "\n",
    "# set frequency range\n",
    "freq_range = td.FreqRange.from_wvl(wvl0=lda0, wvl_width=wvl_width)\n",
    "\n",
    "# wavelengths corresponding to frequency points\n",
    "wvls = freq_range.wvls(num_points=101, spacing=\"uniform_freq\")[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c89d573",
   "metadata": {},
   "source": [
    "The coupler is designed on an X-cut thin film LN. The waveguides are oriented perpendicular to the optical axis. We plan to define our waveguides to be oriented in the $x$ direction in our simulation. Therefore, LN is defined as an [AnisotropicMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.AnisotropicMedium.html) with the extraordinary refractive index in the $y$ direction.\n",
    "\n",
    "In the wavelength range of interest, both LN and SiO$_2$ have a constant refractive index so we model them as non-dispersive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "115ba800",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_e = 2.138  # extraordinary refractive index of LN\n",
    "n_o = 2.211  # ordinary refractive index of LN\n",
    "\n",
    "# define LN medium\n",
    "LN_e = td.Medium(permittivity=n_e**2)\n",
    "LN_o = td.Medium(permittivity=n_o**2)\n",
    "LN = td.AnisotropicMedium(xx=LN_o, yy=LN_e, zz=LN_o)\n",
    "\n",
    "n_sio2 = 1.44  # refractive index of SiO2\n",
    "\n",
    "# define SiO2 medium\n",
    "SiO2 = td.Medium(permittivity=n_sio2**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e355f5a5",
   "metadata": {},
   "source": [
    "Define geometric parameters. The length of the coupler is **2 mm**. The LN waveguide width and thickness are 1 $\\mu m$ and 600 nm, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4c0d311",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = 2000  # length of the coupler\n",
    "d23 = 1  # spacing between the 2nd and the 3rd waveguide at the start of the coupler\n",
    "d12 = 2  # spacing between the 1st and the 2nd waveguide at the start of the coupler\n",
    "w = 1  # waveguide width\n",
    "h = 0.6  # waveguide thickness\n",
    "\n",
    "buffer = 10  # buffer spacing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180d0c00",
   "metadata": {},
   "source": [
    "Defining the geometries for the coupler is relatively easy as it only contains three straight waveguides. For convenience, we copy the `straight_waveguide` method from the list of [common integrated photonic components](https://www.flexcompute.com/tidy3d/examples/notebooks/PICComponents/) and use it to define the waveguides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eccfdf78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def straight_waveguide(x0, y0, z0, x1, y1, wg_width, wg_thickness, medium, sidewall_angle=0):\n",
    "    \"\"\"\n",
    "    This function defines a straight strip waveguide and returns the tidy3d structure of it.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x0: x coordinate of the waveguide starting position (um)\n",
    "    y0: y coordinate of the waveguide starting position (um)\n",
    "    z0: z coordinate of the waveguide starting position (um)\n",
    "    x1: x coordinate of the waveguide end position (um)\n",
    "    y1: y coordinate of the waveguide end position (um)\n",
    "    wg_width: width of the waveguide (um)\n",
    "    wg_thickness: thickness of the waveguide (um)\n",
    "    medium: medium of the waveguide\n",
    "    sidewall_angle: side wall angle of the waveguide (rad)\n",
    "    \"\"\"\n",
    "\n",
    "    cell = gdstk.Cell(\"waveguide\")  # define a gds cell\n",
    "\n",
    "    path = gdstk.RobustPath((x0, y0), wg_width, layer=1, datatype=0)  # define a path\n",
    "    path.segment((x1, y1))\n",
    "\n",
    "    cell.add(path)  # add path to the cell\n",
    "\n",
    "    # define geometry from the gds cell\n",
    "    wg_geo = td.PolySlab.from_gds(\n",
    "        cell,\n",
    "        gds_layer=1,\n",
    "        axis=2,\n",
    "        slab_bounds=(z0 - wg_thickness / 2, z0 + wg_thickness / 2),\n",
    "        sidewall_angle=sidewall_angle,\n",
    "    )\n",
    "\n",
    "    # define tidy3d structure of the bend\n",
    "    wg = td.Structure(geometry=wg_geo[0], medium=medium)\n",
    "\n",
    "    return wg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd3a143",
   "metadata": {},
   "source": [
    "The three waveguides are simply defined by calling the `straight_waveguide` method with the respective start and end coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c05c563",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the first waveguide\n",
    "wg1 = straight_waveguide(\n",
    "    x0=-2 * buffer, y0=0, z0=0, x1=L + 2 * buffer, y1=0, wg_width=w, wg_thickness=h, medium=LN\n",
    ")\n",
    "\n",
    "# define the second waveguide\n",
    "wg2 = straight_waveguide(x0=0, y0=d12, z0=0, x1=L, y1=d23, wg_width=w, wg_thickness=h, medium=LN)\n",
    "\n",
    "# define the third waveguide\n",
    "wg3 = straight_waveguide(\n",
    "    x0=-2 * buffer,\n",
    "    y0=d12 + d23,\n",
    "    z0=0,\n",
    "    x1=L + 2 * buffer,\n",
    "    y1=d12 + d23,\n",
    "    wg_width=w,\n",
    "    wg_thickness=h,\n",
    "    medium=LN,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98709cf",
   "metadata": {},
   "source": [
    "Define source and monitors. Similar to other [PIC examples](https://www.flexcompute.com/tidy3d/examples/notebooks/YJunction/), we will use a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) to launch the waveguide mode at the first waveguide. More specifically, we will examine both the TE0 and TM0 modes and compute the coupling efficiencies. In the [ModeSpec](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSpec.html), we only search for one mode as the TE0 and TM0 modes will be determined by the symmetry of the simulation as we will discuss later.\n",
    "\n",
    "A [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) is defined at the end of the third waveguide to capture the coupling efficiency and a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) is defined in the $z=0$ plane to help visualize the field propagation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce2e29f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a mode source as excitation\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_e)\n",
    "mode_source = td.ModeSource(\n",
    "    center=(-buffer / 4, 0, 0),\n",
    "    size=(0, 3 * w, 6 * h),\n",
    "    source_time=freq_range.to_gaussian_pulse(),\n",
    "    direction=\"+\",\n",
    "    mode_spec=mode_spec,\n",
    "    mode_index=0,\n",
    ")\n",
    "\n",
    "\n",
    "# add a mode monitor to measure transmission at the output waveguide\n",
    "mode_monitor = td.ModeMonitor(\n",
    "    center=(L + buffer / 4, d12 + d23, 0),\n",
    "    size=(0, 3 * w, 6 * h),\n",
    "    freqs=freq_range.freqs(num_points=101),\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode\",\n",
    ")\n",
    "\n",
    "# add a field monitor to visualize field distribution at z=t/2\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=freq_range.freqs(num_points=1),\n",
    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
    "    name=\"field\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6bac8e1",
   "metadata": {},
   "source": [
    "We first define a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) for the TE0 mode by putting what we defined earlier together. Since the LN waveguides are surrounded by SiO$_2$, we can see that there is mirror symmetry with respect to the $z=0$ plane. The TE0 mode has a symmetry of `(0,0,1)` while the TM0 mode has a symmetry of `(0,0,-1)`. Therefore, the TM0 simulation is then simply defined by copying the TE0 simulation and updating the symmetry to `(0,0,-1)`.\n",
    "\n",
    "Note that since the device has a large length, the simulation run time needs to be sufficiently long for the launched mode to propagate and decay. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84b1ddac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define simulation domain size\n",
    "Lx = L + 2 * buffer\n",
    "Ly = d12 + d23 + w + 2 * td.C_0 / freq_range.freq0\n",
    "Lz = 4 * h\n",
    "sim_size = (Lx, Ly, Lz)\n",
    "\n",
    "run_time = 4e-11  # simulation run time\n",
    "\n",
    "# construct te0 simulation\n",
    "sim_te = td.Simulation(\n",
    "    center=(L / 2, (d12 + d23) / 2, 0),\n",
    "    size=sim_size,\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=12, wavelength=td.C_0 / freq_range.freq0),\n",
    "    structures=[wg1, wg2, wg3],\n",
    "    sources=[mode_source],\n",
    "    monitors=[mode_monitor, field_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "    medium=SiO2,\n",
    "    symmetry=(0, 0, 1),\n",
    ")\n",
    "\n",
    "# define tm0 simulation by copying the te0 simulation and update the symmetry\n",
    "sim_tm = sim_te.copy(update={\"symmetry\": (0, 0, -1)})\n",
    "\n",
    "# plot the simulation to visualize the setup\n",
    "ax = sim_te.plot(z=0)\n",
    "ax.set_aspect(\"auto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c99272",
   "metadata": {},
   "source": [
    "## Running Simulation Batch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c3f15b",
   "metadata": {},
   "source": [
    "We can run the two simulations sequentially. However, since both simulations are large, it would be much more efficient if we run them concurrently. To do so, we first define a simulation [Batch](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Batch.html) as demonstrated in the [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/). \n",
    "\n",
    "Before submitting the simulations to run on the server, we estimate the cost to ensure it is reasonable and affordable. This cost is the maximum cost assuming all time steps are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "563f8c34",
   "metadata": {},
   "outputs": [
    {
     "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\">14:41:17 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.450</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:17 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m28.450\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sims = {\n",
    "    \"TE\": sim_te,\n",
    "    \"TM\": sim_tm,\n",
    "}\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "estimate_cost = batch.estimate_cost()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc118dae",
   "metadata": {},
   "source": [
    "Once we confirm that the cost is correct, we run the batch of simulations in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ddac33a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c481d9a276545ea9c260d1901ea09b7",
       "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\">             </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;36m            \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\">14:41:20 EDT </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.450</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:20 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m28.450\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 the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \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 the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0389f36af360427cb42656af788dc499",
       "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\">14:41:21 EDT </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:21 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"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0bee2029e798497a88ecb074229802c1",
       "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"
    }
   ],
   "source": [
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f69d85",
   "metadata": {},
   "source": [
    "After the simulations are complete, we can get the real cost. Since the simulations automatically shut off after field decay reached the threshold, the real cost is lower than the estimated cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c03ce27",
   "metadata": {},
   "outputs": [
    {
     "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\">14:41:35 EDT </span>Total billed flex credit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">24.436</span>.                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m14:41:35 EDT\u001b[0m\u001b[2;36m \u001b[0mTotal billed flex credit cost: \u001b[1;36m24.436\u001b[0m.                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_cost = batch.real_cost()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d709900",
   "metadata": {},
   "source": [
    "## Result Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf6a974",
   "metadata": {},
   "source": [
    "First, let's plot the field distribution for both the TE0 and TM0 simulations.\n",
    "\n",
    "From the plots, we can see the efficient light transfer from one waveguide to the other in both polarizations with minimum excitation of the middle waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1af4cf04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 6), tight_layout=True)\n",
    "\n",
    "batch_results[\"TE\"].plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", vmin=0, vmax=50, ax=ax1\n",
    ")\n",
    "ax1.set_aspect(\"auto\")\n",
    "ax1.set_title(\"TE mode\")\n",
    "batch_results[\"TM\"].plot_field(\n",
    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", vmin=0, vmax=50, ax=ax2\n",
    ")\n",
    "ax2.set_aspect(\"auto\")\n",
    "ax2.set_title(\"TM mode\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf418403",
   "metadata": {},
   "source": [
    "Lastly, we quantitatively calculate and plot the coupling efficiencies. Within the 200 nm simulated bandwidth, we observe a >90% coupling efficiency for both polarizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14127906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the transmission data from the mode monitor\n",
    "amp = batch_results[\"TE\"][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "T_te = np.abs(amp) ** 2\n",
    "\n",
    "amp = batch_results[\"TM\"][\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
    "T_tm = np.abs(amp) ** 2\n",
    "\n",
    "plt.plot(wvls, T_te, c=\"blue\", label=\"TE\", linewidth=3)\n",
    "plt.plot(wvls, T_tm, \"--\", c=\"red\", label=\"TM\", linewidth=3)\n",
    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
    "plt.ylabel(\"Coupling efficiency\")\n",
    "plt.ylim(0.5, 1.05)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2d35a7",
   "metadata": {},
   "source": [
    "Further simulations can be used to test fabrication tolerance, nonzero sidewall angle, and so on, which we will not explicitly demonstrate here for the sake of brevity."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates the simulation of a thin film lithium nobate adiabatic waveguide coupler.",
  "feature_image": "./img/LN_adiabatic_coupler.png",
  "features": [
   "Parameter sweep",
   "Anisotropic material"
  ],
  "kernelspec": {
   "display_name": "test_examp_env",
   "language": "python",
   "name": "python3"
  },
  "keywords": "LN, lithium niobate, coupler, 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": "3.11.13"
  },
  "nbdime-conflicts": {
   "local_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "key": 0,
         "length": 1,
         "op": "removerange"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ],
   "remote_diff": [
    {
     "diff": [
      {
       "diff": [
        {
         "diff": [
          {
           "key": 5,
           "op": "addrange",
           "valuelist": "12"
          },
          {
           "key": 5,
           "length": 1,
           "op": "removerange"
          }
         ],
         "key": 0,
         "op": "patch"
        }
       ],
       "key": "version",
       "op": "patch"
      }
     ],
     "key": "language_info",
     "op": "patch"
    }
   ]
  },
  "title": "Lithium Nobate Adiabatic Waveguide Coupler | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 5
}