{
"cells": [
{
"cell_type": "markdown",
"id": "798fc116",
"metadata": {},
"source": [
"# Waveguide size converter"
]
},
{
"cell_type": "markdown",
"id": "6914938e",
"metadata": {},
"source": [
"Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n",
"\n",
"It is common to have waveguide components of different widths and potentially thicknesses on a photonic integrated circuit (PIC). Therefore, having a low-loss waveguide size converter becomes a necessity. The most common and simple size converter is adiabatic waveguide tapers. However, to achieve low loss and meet the adiabatic condition, the taper inevitable needs to be very long, which is not ideal in many modern high-density PIC designs. To aleviate this shortcoming of the conventional adiabatic taper, novel designs of compact size converter have emerged. \n",
"\n",
"In this notebook, we aim to simulate different types of size converters and compare their performance. We first simulate linear adiabatic tapers of different lengths. Subsequently, we will demonstrate two compact designs: one based on Luneburg lens and the other based on semi-lens emerged from segment optimization. These novel designs achieve ~-0.5 dB loss while being only about 6$\\lambda_0$ in footprint. Linear adiabatic taper can only achieve similar performance while being 30$\\lambda_0$ long."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "36de9cc7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:55.643177Z",
"iopub.status.busy": "2023-03-28T04:06:55.642733Z",
"iopub.status.idle": "2023-03-28T04:06:57.044545Z",
"shell.execute_reply": "2023-03-28T04:06:57.043896Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tidy3d as td\n",
"\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.mode import ModeSolver\n"
]
},
{
"cell_type": "markdown",
"id": "5d524f20",
"metadata": {},
"source": [
"To suppress unnecessary warnings, we set the logging level to `\"ERROR\"`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7c3bcbfb",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:57.047066Z",
"iopub.status.busy": "2023-03-28T04:06:57.046749Z",
"iopub.status.idle": "2023-03-28T04:06:57.068907Z",
"shell.execute_reply": "2023-03-28T04:06:57.068404Z"
}
},
"outputs": [],
"source": [
"td.config.logging_level = \"ERROR\"\n"
]
},
{
"cell_type": "markdown",
"id": "8e88f8a9",
"metadata": {},
"source": [
"Define the simulation wavelength (frequency) range."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d8828055",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:57.070933Z",
"iopub.status.busy": "2023-03-28T04:06:57.070786Z",
"iopub.status.idle": "2023-03-28T04:06:57.090609Z",
"shell.execute_reply": "2023-03-28T04:06:57.090067Z"
}
},
"outputs": [],
"source": [
"lda0 = 1.55 # central wavelength\n",
"freq0 = td.C_0 / lda0 # central frequency\n",
"ldas = np.linspace(1.5, 1.6, 101) # wavelength range\n",
"freqs = td.C_0 / ldas # frequency range\n",
"fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the frequency distribution\n"
]
},
{
"cell_type": "markdown",
"id": "52105151",
"metadata": {},
"source": [
"All devices simulated in this notebook are based on silicon waveguide on a thick oxide layer. We define both materials as nondispersive since the simulation wavelength range is relatively small."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c3086ea4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:57.092846Z",
"iopub.status.busy": "2023-03-28T04:06:57.092678Z",
"iopub.status.idle": "2023-03-28T04:06:57.112959Z",
"shell.execute_reply": "2023-03-28T04:06:57.112370Z"
}
},
"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": "28da9857",
"metadata": {},
"source": [
"## Linear Taper "
]
},
{
"cell_type": "markdown",
"id": "67db0883",
"metadata": {},
"source": [
"The most straightforward way to connect two waveguides of different widths is via an adiabatic taper. The taper shape can be linear, hyperbolic, Gaussian, and so on. Here, we demonstrate a linear taper and investigate how the loss scales with taper length. To be specific, we model a taper connecting the input waveguide of 10 $\\mu m$ wide to an output waveguide of 500 nm wide. Both waveguides have the same thickness of 110 nm. TE0 mode is launched at the input waveguide.\n",
"\n",
"Since we would like to perform a parameter sweep over the taper length, we will define a function called `linear_taper_sim` to construct the simulation. This function will be called repeatedly to make a simulation batch. The structure of the taper including the input and output waveguides can be conveniently made using as a [PolySlab](../_autosummary/tidy3d.PolySlab.html?highlight=polyslab).\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7e2e3c7",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:57.115384Z",
"iopub.status.busy": "2023-03-28T04:06:57.115222Z",
"iopub.status.idle": "2023-03-28T04:06:57.139515Z",
"shell.execute_reply": "2023-03-28T04:06:57.138940Z"
}
},
"outputs": [],
"source": [
"w_in = 10 # input waveguied width\n",
"w_out = 0.5 # output waveguide width\n",
"t_wg = 0.11 # waveguide thickness\n",
"inf_eff = 1e3 # effective infinity of the model\n",
"\n",
"# define the substrate structure\n",
"sub = td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
" ),\n",
" medium=sio2,\n",
")\n",
"\n",
"# define a function to construct the simulation given the taper length\n",
"def linear_taper_sim(L_t):\n",
"\n",
" # vertices of the taper\n",
" vertices = [\n",
" [-inf_eff, w_in / 2],\n",
" [0, w_in / 2],\n",
" [L_t, w_out / 2],\n",
" [inf_eff, w_out / 2],\n",
" [inf_eff, -w_out / 2],\n",
" [L_t, -w_out / 2],\n",
" [0, -w_in / 2],\n",
" [-inf_eff, -w_in / 2],\n",
" ]\n",
"\n",
" # construct the taper structure using a PolySlab\n",
" linear_taper = td.Structure(\n",
" geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t_wg)),\n",
" medium=si,\n",
" )\n",
"\n",
" # add a mode source that launches the TE0 mode at the input waveguide\n",
" mode_source = td.ModeSource(\n",
" center=(-lda0 / 2, 0, t_wg / 2),\n",
" size=(0, 1.2 * w_in, 6 * t_wg),\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" direction=\"+\",\n",
" mode_spec=td.ModeSpec(num_modes=1, target_neff=n_si),\n",
" mode_index=0,\n",
" )\n",
"\n",
" # add a field monitor to visualize the field distribution at z=t_wg/2\n",
" field_monitor = td.FieldMonitor(\n",
" center=(0, 0, t_wg / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
" )\n",
"\n",
" # add a flux monitor to measure transmission to the output waveguide\n",
" flux_monitor = td.FluxMonitor(\n",
" center=(lda0 / 2 + L_t, 0, t_wg / 2),\n",
" size=(0, 2 * w_out, 6 * t_wg),\n",
" freqs=freqs,\n",
" name=\"flux\",\n",
" )\n",
"\n",
" # define simulation domain size\n",
" Lx = L_t + 2 * lda0\n",
" Ly = w_in + 2 * lda0\n",
" Lz = t_wg + 1.5 * lda0\n",
" sim_size = (Lx, Ly, Lz)\n",
"\n",
" run_time = 3e-12 # run time of the simulation\n",
"\n",
" # define simulation\n",
" sim = td.Simulation(\n",
" center=(L_t / 2, 0, t_wg),\n",
" size=sim_size,\n",
" grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n",
" structures=[linear_taper, sub],\n",
" sources=[mode_source],\n",
" monitors=[field_monitor, flux_monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(\n",
" boundary=td.PML()\n",
" ), # pml is used in all boundaries\n",
" symmetry=(0, -1, 0),\n",
" ) # a pec symmetry plane at y=0 can be used to reduce the grid points of the simulation\n",
" return sim\n"
]
},
{
"cell_type": "markdown",
"id": "1e504189",
"metadata": {},
"source": [
"With the `linear_taper_sim` function defined, we can use it to construct a simulation batch. We aim to simulate taper lengths of 10 $\\mu m$, 20 $\\mu m$, 50 $\\mu m$, and 100 $\\mu m$.\n",
"\n",
"To visually verify the simulation setup, we can take a simulation from the batch and use the `plot` method."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d479a55f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-28T04:06:57.142133Z",
"iopub.status.busy": "2023-03-28T04:06:57.141974Z",
"iopub.status.idle": "2023-03-28T04:06:57.499070Z",
"shell.execute_reply": "2023-03-28T04:06:57.498571Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"