{
"cells": [
{
"cell_type": "markdown",
"id": "f10c8f26",
"metadata": {},
"source": [
"# 8-channel mode and polarization (de)multiplexer"
]
},
{
"cell_type": "markdown",
"id": "106c2164",
"metadata": {},
"source": [
"Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n",
"\n",
"Mode-division multiplexing and polarization-division multiplexing on integrated photonic circuits are critical for large-bandwidth and high-speed optical communication networks. Here we introduce an 8-channel mode and polarization (de)multiplexer that is based on asymmetric directional couplers that operate on TE0 to TE3 as well as TM0 to TM3 modes. The design is based on [Wang, J., He, S. and Dai, D. (2014), On-chip silicon 8-channel hybrid (de)multiplexer enabling simultaneous mode- and polarization-division-multiplexing. Laser & Photonics Reviews, 8: L18-L22](https://onlinelibrary.wiley.com/doi/full/10.1002/lpor.201300157).\n",
"\n",
"The notebook is organized as the following: \n",
"\n",
"First, we use Tidy3D's [ModeSolver](../_autosummary/tidy3d.plugins.ModeSolver.html?highlight=modesolver) to simulate the effective indices of the eight modes as a function of waveguide width. From the result, we can obtain the width of the bus waveguide in each directional coupler section to satisfy the phase match condition. \n",
"\n",
"Then, we model each directional coupler section individually to ensure good mode conversion efficiency. \n",
"\n",
"Lastly, once we confirm that good performance is achieved on each section, we build the whole 8-channel (de)multiplexer and simulate the whole device, which is about 200 $\\mu m$ in length. Thanks to the fast speed of Tidy3D solver, large simulations like these can be handled easily. \n",
"\n",
"The models in this notebook contain many waveguide bends. These bends can be defined natively in Tidy3D as demonstrated in the [Euler waveguide bend](../notebooks/EulerWaveguideBend.html) example or the [waveguide Y junction](../notebooks/YJunction.html) example. Alternatively, it is often easier to make use of `gdstk` as shown in the [GDSII import tutorial](../notebooks/GDS_import.html). Here we will also demonstrate how to use `gdstk` to define the structures used in the simulation.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f1046476",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:28.625535Z",
"iopub.status.busy": "2023-03-28T00:00:28.625347Z",
"iopub.status.idle": "2023-03-28T00:00:30.219752Z",
"shell.execute_reply": "2023-03-28T00:00:30.219054Z"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"import gdstk\n",
"\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.mode import ModeSolver\n"
]
},
{
"cell_type": "markdown",
"id": "7bb676ae",
"metadata": {},
"source": [
"The [ModeSolver](../_autosummary/tidy3d.plugins.ModeSolver.html?highlight=modesolver) will check if the solved mode fields decay at the boundaries. When the field does not decay, a warning will be thrown. When we solve for more modes than the waveguide can support, spurious modes will appear and their fields most likely won't decay at the boundaries. We set the logging level to `\"ERROR\"` mainly to avoid these warnings. \n",
"\n",
"In general, we do not recommend setting the logging level to `\"ERROR\"` since most warnings are informative and can help troubleshoot your simulations."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c5c1d71d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.223412Z",
"iopub.status.busy": "2023-03-28T00:00:30.222946Z",
"iopub.status.idle": "2023-03-28T00:00:30.245883Z",
"shell.execute_reply": "2023-03-28T00:00:30.245313Z"
},
"tags": []
},
"outputs": [],
"source": [
"td.config.logging_level = \"ERROR\"\n"
]
},
{
"cell_type": "markdown",
"id": "a7d335b5",
"metadata": {},
"source": [
"## Mode Indices Calculation"
]
},
{
"cell_type": "markdown",
"id": "5e4cae7f",
"metadata": {},
"source": [
"To obtain the width of the bus waveguide in each asymmetric directional coupler, we need to first calculate the relationship between the effective indices of each mode and the waveguide width. This can be achieved by using the [ModeSolver](../_autosummary/tidy3d.plugins.ModeSolver.html?highlight=modesolver) from Tidy3D's plugins. This computation will be done on a local computer so it won't cost any FlexCredits.\n",
"\n",
"For the entire notebook, we will focus on the central wavelength of 1550 nm and a wavelength range from 1500 nm to 1600 nm.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d91ebe00",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.248053Z",
"iopub.status.busy": "2023-03-28T00:00:30.247888Z",
"iopub.status.idle": "2023-03-28T00:00:30.271833Z",
"shell.execute_reply": "2023-03-28T00:00:30.271198Z"
},
"tags": []
},
"outputs": [],
"source": [
"lda0 = 1.55 # central wavelength\n",
"ldas = np.linspace(1.5, 1.6, 101) # wavelength range of interest\n",
"\n",
"freq0 = td.C_0 / lda0 # corresponding central frequency\n",
"freqs = td.C_0 / ldas # corresponding frequency range\n",
"\n",
"fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the excitation spectrum\n"
]
},
{
"cell_type": "markdown",
"id": "2b036b2a",
"metadata": {},
"source": [
"The structure is Si waveguide on silica substrate and top cladding. Therefore, we only need to define two materials. Within the wavelength range of interest, they can be considered lossless and dispersionless."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5b23e8e9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.274385Z",
"iopub.status.busy": "2023-03-28T00:00:30.274170Z",
"iopub.status.idle": "2023-03-28T00:00:30.295995Z",
"shell.execute_reply": "2023-03-28T00:00:30.295415Z"
},
"tags": []
},
"outputs": [],
"source": [
"n_si = 3.48 # silicon refractive index\n",
"si = td.Medium(permittivity=n_si**2)\n",
"\n",
"n_sio2 = 1.44 # silicon oxide refractive index\n",
"sio2 = td.Medium(permittivity=n_sio2**2)\n"
]
},
{
"cell_type": "markdown",
"id": "31f7f875",
"metadata": {},
"source": [
"The thickness of the waveguide is the standard 220 nm, which we will use throughout the notebook.\n",
"\n",
"Here we calculate the effective indices for the four TE modes (TE0-TE3) and the four TM modes (TM0-TM3) for waveguide width from 300 nm to 2500 nm."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "472af715",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.298236Z",
"iopub.status.busy": "2023-03-28T00:00:30.298076Z",
"iopub.status.idle": "2023-03-28T00:00:30.319809Z",
"shell.execute_reply": "2023-03-28T00:00:30.319162Z"
},
"tags": []
},
"outputs": [],
"source": [
"h = 0.22 # waveguide thickness\n",
"ws = np.linspace(0.3, 2.5, 30) # range of waveguide width\n"
]
},
{
"cell_type": "markdown",
"id": "55b3bd07",
"metadata": {},
"source": [
"Define [ModeSpec](../_autosummary/tidy3d.ModeSpec.html?highlight=modespec), [GridSpec](../_autosummary/tidy3d.GridSpec.html), and [BoudnarySpec](../_autosummary/tidy3d.BoundarySpec.html?highlight=boundaryspec) for the simulations. The number of modes is set to 4 to make sure TE0-TE3 (TM0-TM3) modes are always included, even though when the waveguide width is small, not all modes are supported. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3fbb79f9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.323489Z",
"iopub.status.busy": "2023-03-28T00:00:30.322929Z",
"iopub.status.idle": "2023-03-28T00:00:30.345336Z",
"shell.execute_reply": "2023-03-28T00:00:30.344804Z"
},
"tags": []
},
"outputs": [],
"source": [
"N_mode = 4 # number of modes\n",
"\n",
"# define mode spec\n",
"mode_spec = td.ModeSpec(num_modes=N_mode, target_neff=n_si)\n",
"\n",
"# define grid spec\n",
"grid_spec = td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0)\n",
"\n",
"# define boundary spec\n",
"bound_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n"
]
},
{
"cell_type": "markdown",
"id": "faee33d5",
"metadata": {},
"source": [
"The waveguide structure has a mirror symmetry with respect to the $xy$ plane. In this case, the TE and TM modes share different symmetries. Therefore, we can solve for the TE and TM separately by imposing the corresponding symmetry in the simulation. This way, the result looks cleaner.\n",
"\n",
"First, we use the `(0,0,1)` symmetry to get the effective indices of the TE modes. A for loop will be used to iterate over different waveguide widths. At each iteration, the effective indices of the first four TE modes will be calculated."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a8765bc5",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:00:30.347765Z",
"iopub.status.busy": "2023-03-28T00:00:30.347607Z",
"iopub.status.idle": "2023-03-28T00:01:10.644023Z",
"shell.execute_reply": "2023-03-28T00:01:10.643388Z"
},
"tags": []
},
"outputs": [],
"source": [
"n_eff = np.zeros((len(ws), N_mode)) # placeholder for the effective indices\n",
"\n",
"# loop over the waveguide width and compute the effective indices at each iteration\n",
"for i, w in enumerate(ws):\n",
"\n",
" # define the waveguide structure\n",
" waveguide = td.Structure(\n",
" geometry=td.Box(center=(0, 0, 0), size=(w, td.inf, h)), medium=si\n",
" )\n",
"\n",
" sim_size = (6 * w, 0, 8 * h) # simulation domain size\n",
"\n",
" # define simulation\n",
" sim = td.Simulation(\n",
" size=sim_size,\n",
" grid_spec=grid_spec,\n",
" structures=[waveguide],\n",
" sources=[],\n",
" monitors=[],\n",
" run_time=1e-11,\n",
" boundary_spec=bound_spec,\n",
" medium=sio2,\n",
" symmetry=(0, 0, 1),\n",
" )\n",
"\n",
" # define mode solver\n",
" mode_solver = ModeSolver(\n",
" simulation=sim,\n",
" plane=td.Box(center=(0, 0, 0), size=sim_size),\n",
" mode_spec=mode_spec,\n",
" freqs=[freq0],\n",
" )\n",
"\n",
" # solve for the modes\n",
" mode_data = mode_solver.solve()\n",
"\n",
" # obtain the effective indices\n",
" n_eff[i] = mode_data.n_eff.values\n"
]
},
{
"cell_type": "markdown",
"id": "1a2ffc2d",
"metadata": {},
"source": [
"After the indices are solved, we can plot them for visualization."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "87edbe05",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T00:01:10.646700Z",
"iopub.status.busy": "2023-03-28T00:01:10.646466Z",
"iopub.status.idle": "2023-03-28T00:01:10.910560Z",
"shell.execute_reply": "2023-03-28T00:01:10.909939Z"
},
"tags": []
},
"outputs": [
{
"data": {
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\n",
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