{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Genetic algorithm optimization of a silicon on-chip reflector\n",
"\n",
"**Note: the cost of running the entire notebook is larger than 10\n",
"FlexCredits.**\n",
"\n",
"A [genetic algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm)\n",
"(GA) is a search heuristic that mimics the process of natural selection.\n",
"This algorithm reflects the process of natural evolution where the\n",
"fittest individuals are selected for reproduction in order to produce\n",
"offspring of the next generation.\n",
"\n",
"The steps in a GA are typically as follows:\n",
"\n",
"1. **Initial Population**: The process begins with a set of individuals\n",
" which is called a population. Each individual is a solution to the\n",
" problem you want to solve.\n",
"\n",
"2. **Fitness Function**: Each individual in the population has a\n",
" fitness score which indicates how good it is at solving the problem.\n",
"\n",
"3. **Selection**: The algorithm selects individuals, often the fittest\n",
" among the population, to breed a new generation. The selection can\n",
" be done in various ways, such as roulette wheel selection,\n",
" tournament selection, etc.\n",
"\n",
"4. **Crossover**: During crossover, parts of two individuals’\n",
" chromosome strings are swapped to get a new offspring, which may\n",
" contain some parts of both parents’ strings. This simulates\n",
" reproduction and biological crossover.\n",
"\n",
"5. **Mutation**: In some new offspring, random genes are mutated or\n",
" changed to maintain diversity within the population and to avoid\n",
" premature convergence.\n",
"\n",
"6. **New Generation**: The new generation of population is formed by\n",
" the offspring. This new generation is then used in the next\n",
" iteration of the algorithm.\n",
"\n",
"7. **Termination**: The algorithm terminates if the population has\n",
" converged (does not produce offspring that are significantly\n",
" different from the previous generation), or a satisfactory solution\n",
" has been found, or a set number of generations have been produced.\n",
"\n",
"GAs have become a powerful tool for optimizing photonic components,\n",
"leveraging their ability to efficiently search large and complex design\n",
"spaces. In photonics, where the performance of components like\n",
"waveguides, photonic crystals, and fibers can be highly sensitive to\n",
"geometrical and material parameters, GAs offer a way to find optimal\n",
"solutions that might be difficult to discover using traditional design\n",
"methods.\n",
"\n",
"In this notebook, we demonstrate the optimization of a compact silicon\n",
"waveguide reflector using GA, utilizing the open source Python library\n",
"[pyGAD](https://pygad.readthedocs.io/en/latest/) implemented into the\n",
"`Tidy3D` Design plugin. The idea follows\n",
"`Zejie Yu, Haoran Cui, and Xiankai Sun, \"Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities,\" Photon. Res. 5, B15-B19 (2017)`\n",
"[DOI:10.1364/PRJ.5.000B15](https://doi.org/10.1364/PRJ.5.000B15). The\n",
"design region of the reflector consists of a grid divided into 18x18\n",
"pixels. Each pixel is a square measuring 120 nm by 120 nm.\n",
"\n",
"![\"Schematic](\"img/optimized_reflector.png\")
\n",
"\n",
"`Tidy3D` is a powerful tool for photonic design optimization due to its\n",
"fast speed and high throughput. Besides GA, we have demonstrated\n",
"particle swarm optimizations of a [polarization beam\n",
"splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
"and a [bullseye\n",
"cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/),\n",
"CMA-ES optimization of an [waveguide\n",
"S-bend](https://www.flexcompute.com/tidy3d/examples/notebooks/SbendCMAES/),\n",
"Bayesian optimization of a\n",
"[Y-junction](https://www.flexcompute.com/tidy3d/examples/notebooks/BayesianOptimizationYJunction/),\n",
"and direct binary search optimization of an [optical\n",
"switch](https://www.flexcompute.com/tidy3d/examples/notebooks/OpticalSwitchDBS/).\n",
"Furthermore, we also have a growing list of gradient-based adjoint\n",
"optimization examples including\n",
"\n",
"1. [Mode\n",
" converter](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd3InverseDesign/),\n",
"\n",
"2. [Waveguide\n",
" taper](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd5BoundaryGradients/),\n",
"\n",
"3. [Metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd7Metalens/),\n",
"\n",
"4. [Waveguide\n",
" bend](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd8WaveguideBend/),\n",
"\n",
"5. [Multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd9WDM/),\n",
"\n",
"6. [Y-branch](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd10YBranchLevelSet/),\n",
"\n",
"7. [Light\n",
" extractor](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd12LightExtractor/)."
],
"id": "7cb221d6-d51e-48c5-8935-87f72066d44f"
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment the following line to install pygad if it's not installed in your environment already\n",
"# pip install pygad\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tidy3d as td\n",
"import tidy3d.plugins.design as tdd\n",
"import tidy3d.web as web"
],
"id": "8e339837"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation Setup\n",
"\n",
"For simplicity, we will use the silicon and oxide media directly from\n",
"the [material\n",
"library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)."
],
"id": "c80b67a3-6477-46fc-aeac-532141f0ef41"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"Si = td.material_library[\"cSi\"][\"Palik_LowLoss\"]\n",
"SiO2 = td.material_library[\"SiO2\"][\"Palik_LowLoss\"]"
],
"id": "dac7a9d1"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simulation wavelength range is 1450 nm to 1650 nm."
],
"id": "9cb48735-9afa-4ffc-a8c7-8fcf4123b127"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"lda0 = 1.55 # central wavelength\n",
"freq0 = td.C_0 / lda0 # central frequency\n",
"ldas = np.linspace(1.45, 1.65, 101) # wavelength range\n",
"freqs = td.C_0 / ldas # frequency range\n",
"fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency range"
],
"id": "93c995dc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The waveguide has a 500 nm width and 220 nm thickness. The design region\n",
"consists of 18 by 18 pixels. Each pixel is 120 nm by 120 nm. Due to the\n",
"symmetry, we only consider symmetric design so the total number of\n",
"tunable pixels is 18\\*9=162."
],
"id": "3dc09afb-6fb3-49c9-b35a-7c994bdf91ad"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"w = 0.5 # width of the waveguide\n",
"t = 0.22 # thickness of the silicon\n",
"l = 1 # length of the waveguide in the simulation\n",
"Px = Py = 0.12 # pixel sizes in the x and y directions\n",
"Nx = 18 # number of pixels in the x direction\n",
"Ny = 9 # number of pixels in the y direction\n",
"buffer = 0.8 # buffer spacing\n",
"res = 15 # overall resolution setting (steps per wavelength)\n",
"gsx = gsy = 5 # number of grid steps per pixel in override region"
],
"id": "94d7fbc0"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will create an array of length 162 to represent a design. Each\n",
"element corresponds to each pixel in the design region. An element value\n",
"of 1 means the pixel is silicon while an element value of 0 means the\n",
"pixel is void. To facilitate the optimization, we define a helper\n",
"function `create_design(pixels)` that takes the pixel array and creates\n",
"the\n",
"[Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html)\n",
"of the simulation, including the waveguide and the design region."
],
"id": "2ba4d662-d882-4ee9-a13b-4a6209ed9566"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def create_design(pixels):\n",
" geo = 0\n",
" for i, pixel in enumerate(pixels):\n",
" if pixel == 1:\n",
" geo += td.Box(\n",
" center=(l + Px / 2 + Px * (i % Nx), Py * Ny - Py / 2 - Py * (i // Nx), t / 2),\n",
" size=(Px, Py, t),\n",
" )\n",
" geo += td.Box(\n",
" center=(l + Px / 2 + Px * (i % Nx), -(Py * Ny - Py / 2 - Py * (i // Nx)), t / 2),\n",
" size=(Px, Py, t),\n",
" )\n",
" geo = geo + td.Box(center=(0, 0, t / 2), size=(2 * l, w, t))\n",
"\n",
" design = td.Structure(geometry=geo, medium=Si)\n",
"\n",
" return design"
],
"id": "f84e1a09"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a quick check, we create a random array and plot the created\n",
"structures."
],
"id": "44b89b1d-6e82-43ec-bdfd-9591b90fc9b3"
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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X22mnnTJ27NjMnz8/PT09m1Szv78/X/va13LXXXdl8eLFGTduXPbb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\ngAMOyMiRI3Prrbfm3nvvzUknnZS77ro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yNc7v6enJAQcckMceeyx7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\n"
}
}
],
"source": [
"np.random.seed(0) # fix a random seed for reproducibility\n",
"pixels = np.random.choice([0, 1], size=Nx * Ny)\n",
"design = create_design(pixels)\n",
"design.plot(z=t / 2)\n",
"plt.show()"
],
"id": "f47016cc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Furthermore, we define a `make_sim(pixels)` function to define the\n",
"entire simulation given the pixel array. The simulation includes a\n",
"[ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html)\n",
"to launch the TE0 mode at the waveguide and a\n",
"[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
"next to the source to measure the reflection. To minimize data download\n",
"during the optimization, we will only record the reflection at the\n",
"central wavelength of 1550 nm since the objective is to maximize this\n",
"value."
],
"id": "1fe2de64-cc8d-44f6-8c62-59bf2fc08568"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def make_sim(pixels):\n",
" design = create_design(pixels)\n",
"\n",
" # Add a mode source as excitation\n",
" mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
" mode_source = td.ModeSource(\n",
" center=(l / 2, 0, t / 2),\n",
" size=(0, 4 * w, 6 * t),\n",
" source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
" direction=\"+\",\n",
" mode_spec=mode_spec,\n",
" mode_index=0,\n",
" )\n",
"\n",
" # Add a mode monitor to measure transmission at the output waveguide\n",
" mode_monitor = td.ModeMonitor(\n",
" center=(l / 4, 0, t / 2),\n",
" size=mode_source.size,\n",
" freqs=[freq0],\n",
" mode_spec=mode_spec,\n",
" name=\"mode\",\n",
" )\n",
"\n",
" # Define simulation domain size\n",
" Lx = l + Nx * Px + buffer\n",
" Ly = 2 * Ny * Py + 2 * buffer\n",
" Lz = 10 * t\n",
"\n",
" eff_inf = 1e2 # effective infinity\n",
"\n",
" # Define substrate structure\n",
" substrate = td.Structure(\n",
" geometry=td.Box.from_bounds(\n",
" rmin=(-eff_inf, -eff_inf, -eff_inf), rmax=(eff_inf, eff_inf, 0)\n",
" ),\n",
" medium=SiO2,\n",
" )\n",
"\n",
" run_time = 5e-13 # simulation run time\n",
"\n",
" # Simulation box\n",
" sim_box = td.Box.from_bounds(rmin=(0, -Ly / 2, -Lz / 2), rmax=(Lx, Ly / 2, Lz / 2))\n",
"\n",
" # Mesh override structure over the pixel region to ensure grid that conforms to the pixels\n",
" mesh_override = td.MeshOverrideStructure(\n",
" geometry=td.Box.from_bounds(rmin=(l, -Py * Ny, 0), rmax=(l + Px * Nx, Py * Ny, 0)),\n",
" dl=(\n",
" Px / gsx,\n",
" Py / gsy,\n",
" 1,\n",
" ), # the z-direction dl doesn't matter as the box size is 0 along z\n",
" )\n",
"\n",
" # Define simulation\n",
" sim = td.Simulation(\n",
" center=sim_box.center,\n",
" size=sim_box.size,\n",
" grid_spec=td.GridSpec.auto(\n",
" min_steps_per_wvl=res, wavelength=lda0, override_structures=[mesh_override]\n",
" ),\n",
" structures=[design, substrate],\n",
" sources=[mode_source],\n",
" monitors=[mode_monitor],\n",
" run_time=run_time,\n",
" boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
" symmetry=(0, -1, 0),\n",
" )\n",
"\n",
" return sim"
],
"id": "192c04c6"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we test it by defining a simulation with a random pixel array.\n",
"From the 3D view, we confirm that the simulation setup is correct."
],
"id": "3e24ef42-cfbe-4a17-9d2a-6e2eb41dd5a0"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
}
}
],
"source": [
"sim = make_sim(pixels)\n",
"sim.plot_3d()"
],
"id": "066715b3"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GA Setup\n",
"\n",
"Now we are ready to setup a GA optimization. This can quickly be done\n",
"using the Tidy3D `Design` plugin, which can efficiently manage the\n",
"optimization with parallel cloud computing, reducing the runtime and\n",
"saving FlexCredits.\n",
"\n",
"First we need to define some hyperparameters. In this particular\n",
"example, we will use 30 solutions per population and run the\n",
"optimization for a total of 25 generations, and also specify early-stop\n",
"criteria that stops the optimization if the fitness value doesn’t change\n",
"for 6 consecutive generations. The `MethodGenAlg` class can be used to\n",
"tune a number of parameters that change the selection, crossover, and\n",
"mutation processes. We only change a few and leave the most to the\n",
"default settings. Users are encouraged to explore these settings using\n",
"the\n",
"[PyGAD](https://pygad.readthedocs.io/en/latest/pygad.html#pygad-ga-class)\n",
"docs and fine tune them for better results. For reproducibility, we also\n",
"set a fixed random seed.\n",
"\n",
"A list `ParameterInt` objects are created to store the binary values\n",
"used to define the geometry of the reflector. These parameters and the\n",
"GA method are supplied to a `DesignSpace` object, which manages the GA\n",
"process and outputs the results of the optimization."
],
"id": "7c2b0396-f8da-4c1a-bc5e-a084886801e7"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"method = tdd.MethodGenAlg(\n",
" solutions_per_pop=30,\n",
" n_generations=25,\n",
" n_parents_mating=10,\n",
" keep_elitism=1,\n",
" parent_selection_type=\"sss\",\n",
" crossover_type=\"single_point\",\n",
" crossover_prob=0.7,\n",
" mutation_type=\"inversion\",\n",
" mutation_prob=None,\n",
" seed=5,\n",
" stop_criteria_type=\"saturate\",\n",
" stop_criteria_number=6,\n",
")\n",
"\n",
"parameters = [tdd.ParameterInt(name=str(i), span=(0, 1)) for i in range(Nx * Ny)]\n",
"\n",
"design_space = tdd.DesignSpace(\n",
" method=method, parameters=parameters, task_name=\"GA_Notebook\", path_dir=\"./data\"\n",
")"
],
"id": "2609ef0a"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can optionally summarize the design space to check the setup."
],
"id": "7d0ac107-0c57-489f-8144-0d8c50fd8482"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:41:37 UTC \n",
" Summary of DesignSpace \n",
" \n",
" Method: MethodGenAlg \n",
" Method Args \n",
" solutions_per_pop: 30 \n",
" n_generations: 25 \n",
" n_parents_mating: 10 \n",
" stop_criteria_type: saturate \n",
" stop_criteria_number: 6.0 \n",
" parent_selection_type: sss \n",
" keep_parents: -1 \n",
" keep_elitism: 1 \n",
" crossover_type: single_point \n",
" crossover_prob: 0.7 \n",
" mutation_type: inversion \n",
" mutation_prob: None \n",
" save_solution: False \n",
" \n",
" No. of Parameters: 162 \n",
" Parameters: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
" 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n",
" 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,\n",
" 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n",
" 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,\n",
" 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, \n",
" 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, \n",
" 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, \n",
" 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, \n",
" 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, \n",
" 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 \n",
" 0: ParameterInt (0, 1) \n",
" 1: ParameterInt (0, 1) \n",
" 2: ParameterInt (0, 1) \n",
" 3: ParameterInt (0, 1) \n",
" 4: ParameterInt (0, 1) \n",
" 5: ParameterInt (0, 1) \n",
" 6: ParameterInt (0, 1) \n",
" 7: ParameterInt (0, 1) \n",
" 8: ParameterInt (0, 1) \n",
" 9: ParameterInt (0, 1) \n",
" 10: ParameterInt (0, 1) \n",
" 11: ParameterInt (0, 1) \n",
" 12: ParameterInt (0, 1) \n",
" 13: ParameterInt (0, 1) \n",
" 14: ParameterInt (0, 1) \n",
" 15: ParameterInt (0, 1) \n",
" 16: ParameterInt (0, 1) \n",
" 17: ParameterInt (0, 1) \n",
" 18: ParameterInt (0, 1) \n",
" 19: ParameterInt (0, 1) \n",
" 20: ParameterInt (0, 1) \n",
" 21: ParameterInt (0, 1) \n",
" 22: ParameterInt (0, 1) \n",
" 23: ParameterInt (0, 1) \n",
" 24: ParameterInt (0, 1) \n",
" 25: ParameterInt (0, 1) \n",
" 26: ParameterInt (0, 1) \n",
" 27: ParameterInt (0, 1) \n",
" 28: ParameterInt (0, 1) \n",
" 29: ParameterInt (0, 1) \n",
" 30: ParameterInt (0, 1) \n",
" 31: ParameterInt (0, 1) \n",
" 32: ParameterInt (0, 1) \n",
" 33: ParameterInt (0, 1) \n",
" 34: ParameterInt (0, 1) \n",
" 35: ParameterInt (0, 1) \n",
" 36: ParameterInt (0, 1) \n",
" 37: ParameterInt (0, 1) \n",
" 38: ParameterInt (0, 1) \n",
" 39: ParameterInt (0, 1) \n",
" 40: ParameterInt (0, 1) \n",
" 41: ParameterInt (0, 1) \n",
" 42: ParameterInt (0, 1) \n",
" 43: ParameterInt (0, 1) \n",
" 44: ParameterInt (0, 1) \n",
" 45: ParameterInt (0, 1) \n",
" 46: ParameterInt (0, 1) \n",
" 47: ParameterInt (0, 1) \n",
" 48: ParameterInt (0, 1) \n",
" 49: ParameterInt (0, 1) \n",
" 50: ParameterInt (0, 1) \n",
" 51: ParameterInt (0, 1) \n",
" 52: ParameterInt (0, 1) \n",
" 53: ParameterInt (0, 1) \n",
" 54: ParameterInt (0, 1) \n",
" 55: ParameterInt (0, 1) \n",
" 56: ParameterInt (0, 1) \n",
" 57: ParameterInt (0, 1) \n",
" 58: ParameterInt (0, 1) \n",
" 59: ParameterInt (0, 1) \n",
" 60: ParameterInt (0, 1) \n",
" 61: ParameterInt (0, 1) \n",
" 62: ParameterInt (0, 1) \n",
" 63: ParameterInt (0, 1) \n",
" 64: ParameterInt (0, 1) \n",
" 65: ParameterInt (0, 1) \n",
" 66: ParameterInt (0, 1) \n",
" 67: ParameterInt (0, 1) \n",
" 68: ParameterInt (0, 1) \n",
" 69: ParameterInt (0, 1) \n",
" 70: ParameterInt (0, 1) \n",
" 71: ParameterInt (0, 1) \n",
" 72: ParameterInt (0, 1) \n",
" 73: ParameterInt (0, 1) \n",
" 74: ParameterInt (0, 1) \n",
" 75: ParameterInt (0, 1) \n",
" 76: ParameterInt (0, 1) \n",
" 77: ParameterInt (0, 1) \n",
" 78: ParameterInt (0, 1) \n",
" 79: ParameterInt (0, 1) \n",
" 80: ParameterInt (0, 1) \n",
" 81: ParameterInt (0, 1) \n",
" 82: ParameterInt (0, 1) \n",
" 83: ParameterInt (0, 1) \n",
" 84: ParameterInt (0, 1) \n",
" 85: ParameterInt (0, 1) \n",
" 86: ParameterInt (0, 1) \n",
" 87: ParameterInt (0, 1) \n",
" 88: ParameterInt (0, 1) \n",
" 89: ParameterInt (0, 1) \n",
" 90: ParameterInt (0, 1) \n",
" 91: ParameterInt (0, 1) \n",
" 92: ParameterInt (0, 1) \n",
" 93: ParameterInt (0, 1) \n",
" 94: ParameterInt (0, 1) \n",
" 95: ParameterInt (0, 1) \n",
" 96: ParameterInt (0, 1) \n",
" 97: ParameterInt (0, 1) \n",
" 98: ParameterInt (0, 1) \n",
" 99: ParameterInt (0, 1) \n",
" 100: ParameterInt (0, 1) \n",
" 101: ParameterInt (0, 1) \n",
" 102: ParameterInt (0, 1) \n",
" 103: ParameterInt (0, 1) \n",
" 104: ParameterInt (0, 1) \n",
" 105: ParameterInt (0, 1) \n",
" 106: ParameterInt (0, 1) \n",
" 107: ParameterInt (0, 1) \n",
" 108: ParameterInt (0, 1) \n",
" 109: ParameterInt (0, 1) \n",
" 110: ParameterInt (0, 1) \n",
" 111: ParameterInt (0, 1) \n",
" 112: ParameterInt (0, 1) \n",
" 113: ParameterInt (0, 1) \n",
" 114: ParameterInt (0, 1) \n",
" 115: ParameterInt (0, 1) \n",
" 116: ParameterInt (0, 1) \n",
" 117: ParameterInt (0, 1) \n",
" 118: ParameterInt (0, 1) \n",
" 119: ParameterInt (0, 1) \n",
" 120: ParameterInt (0, 1) \n",
" 121: ParameterInt (0, 1) \n",
" 122: ParameterInt (0, 1) \n",
" 123: ParameterInt (0, 1) \n",
" 124: ParameterInt (0, 1) \n",
" 125: ParameterInt (0, 1) \n",
" 126: ParameterInt (0, 1) \n",
" 127: ParameterInt (0, 1) \n",
" 128: ParameterInt (0, 1) \n",
" 129: ParameterInt (0, 1) \n",
" 130: ParameterInt (0, 1) \n",
" 131: ParameterInt (0, 1) \n",
" 132: ParameterInt (0, 1) \n",
" 133: ParameterInt (0, 1) \n",
" 134: ParameterInt (0, 1) \n",
" 135: ParameterInt (0, 1) \n",
" 136: ParameterInt (0, 1) \n",
" 137: ParameterInt (0, 1) \n",
" 138: ParameterInt (0, 1) \n",
" 139: ParameterInt (0, 1) \n",
" 140: ParameterInt (0, 1) \n",
" 141: ParameterInt (0, 1) \n",
" 142: ParameterInt (0, 1) \n",
" 143: ParameterInt (0, 1) \n",
" 144: ParameterInt (0, 1) \n",
" 145: ParameterInt (0, 1) \n",
" 146: ParameterInt (0, 1) \n",
" 147: ParameterInt (0, 1) \n",
" 148: ParameterInt (0, 1) \n",
" 149: ParameterInt (0, 1) \n",
" 150: ParameterInt (0, 1) \n",
" 151: ParameterInt (0, 1) \n",
" 152: ParameterInt (0, 1) \n",
" 153: ParameterInt (0, 1) \n",
" 154: ParameterInt (0, 1) \n",
" 155: ParameterInt (0, 1) \n",
" 156: ParameterInt (0, 1) \n",
" 157: ParameterInt (0, 1) \n",
" 158: ParameterInt (0, 1) \n",
" 159: ParameterInt (0, 1) \n",
" 160: ParameterInt (0, 1) \n",
" 161: ParameterInt (0, 1) \n",
" \n",
" Maximum Run Count: 780 \n",
" \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Notes: \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" The maximum run count for MethodGenAlg is difficult to predict. \n",
" Repeated solutions are not executed, reducing the total number of \n",
" simulations. High crossover and mutation probabilities may result \n",
" in an increased number of simulations, potentially exceeding the \n",
" predicted maximum run count. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Discrete 'int' values are automatically rounded if optimizers \n",
" generate 'float' predictions. \n",
" \n",
""
]
}
}
],
"source": [
"summary = design_space.summarize()"
],
"id": "f5f711dc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To run the GA optimization we need to define the `fn_pre` and `fn_post`\n",
"functions for the optimizer. The pre function creates a `Simulation`\n",
"from the parameters suggested by GA as a solution to the reflector. The\n",
"post function defines how the `SimulationData` should be evaluated to\n",
"create a single float value which is then fed back into the GA. By\n",
"evaluating of the float values of the current generation the GA can then\n",
"assemble the population for the next generation, based on our\n",
"hyperparameters."
],
"id": "60a9976b-2b13-4d04-9195-365c9ea53df5"
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def fn_pre(**params):\n",
" pixels = np.array(list(params.values()))\n",
" sim = make_sim(pixels)\n",
"\n",
" return sim\n",
"\n",
"\n",
"def fn_post(sim_data):\n",
" return abs(sim_data[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2"
],
"id": "e6abb553"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once satisfied with the setup, the optimization can be started by\n",
"calling `DesignSpace.run` with our `fn_pre` and `fn_post` functions. The\n",
"`DesignSpace` will parallelize each generation by running the\n",
"`Simulation` objects as a batch, and will prevent previously evaluated\n",
"solutions from being recomputed. Once complete, we can combine all the\n",
"solution outputs within a Pandas `DataFrame` for further analysis."
],
"id": "d91240e6-4b24-4f9a-9d30-2d83eb895bcb"
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:41:38 UTC Running 30 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:42:20 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:42:55 UTC Generation 1 Best Fitness: 0.722 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:42:56 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:43:32 UTC Generation 2 Best Fitness: 0.776 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:43:33 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:44:10 UTC Generation 3 Best Fitness: 0.782 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:44:11 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:44:47 UTC Generation 4 Best Fitness: 0.808 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:44:48 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:45:24 UTC Generation 5 Best Fitness: 0.808 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:45:25 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:46:01 UTC Generation 6 Best Fitness: 0.808 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:46:02 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:47:39 UTC Generation 7 Best Fitness: 0.808 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:47:40 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:48:51 UTC Generation 8 Best Fitness: 0.826 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:48:52 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:49:28 UTC Generation 9 Best Fitness: 0.832 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:49:29 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:50:05 UTC Generation 10 Best Fitness: 0.832 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:50:06 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:50:42 UTC Generation 11 Best Fitness: 0.837 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:50:43 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:51:20 UTC Generation 12 Best Fitness: 0.837 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:51:21 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:51:58 UTC Generation 13 Best Fitness: 0.839 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:51:59 UTC Running 28 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:52:34 UTC Generation 14 Best Fitness: 0.839 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:52:35 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:53:13 UTC Generation 15 Best Fitness: 0.839 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:53:14 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:53:49 UTC Generation 16 Best Fitness: 0.839 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:53:50 UTC Running 28 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:54:24 UTC Generation 17 Best Fitness: 0.854 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:54:25 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:55:01 UTC Generation 18 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:55:02 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:55:38 UTC Generation 19 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:55:39 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:56:14 UTC Generation 20 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:56:15 UTC Running 28 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:56:49 UTC Generation 21 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:56:50 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:57:58 UTC Generation 22 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:57:59 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:59:00 UTC Generation 23 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"04:59:01 UTC Running 29 Simulations \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:01 UTC Generation 24 Best Fitness: 0.872 \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Best Result: 0.8722625438985785 \n",
" Best Parameters: 0: 0 1: 0 2: 1 3: 0 4: 1 5: 1 6: 0 7: 1 8: 1 9: 1 \n",
" 10: 0 11: 0 12: 0 13: 1 14: 0 15: 1 16: 1 17: 0 18: 1 19: 0 20: 1 \n",
" 21: 0 22: 0 23: 1 24: 0 25: 0 26: 0 27: 0 28: 1 29: 1 30: 0 31: 1 \n",
" 32: 0 33: 0 34: 0 35: 0 36: 0 37: 0 38: 0 39: 0 40: 0 41: 1 42: 1 \n",
" 43: 0 44: 0 45: 1 46: 1 47: 0 48: 0 49: 1 50: 1 51: 1 52: 1 53: 1 \n",
" 54: 1 55: 1 56: 0 57: 0 58: 0 59: 0 60: 0 61: 0 62: 0 63: 0 64: 0 \n",
" 65: 0 66: 0 67: 0 68: 1 69: 1 70: 1 71: 1 72: 1 73: 0 74: 1 75: 1 \n",
" 76: 0 77: 1 78: 0 79: 1 80: 0 81: 0 82: 0 83: 1 84: 0 85: 1 86: 0 \n",
" 87: 1 88: 0 89: 0 90: 0 91: 0 92: 0 93: 1 94: 1 95: 0 96: 1 97: 1 \n",
" 98: 0 99: 0 100: 0 101: 1 102: 1 103: 1 104: 0 105: 1 106: 1 107: 0\n",
" 108: 1 109: 0 110: 1 111: 1 112: 1 113: 0 114: 0 115: 1 116: 1 117:\n",
" 1 118: 1 119: 0 120: 0 121: 1 122: 0 123: 1 124: 0 125: 1 126: 1 \n",
" 127: 1 128: 1 129: 0 130: 1 131: 0 132: 1 133: 0 134: 1 135: 0 136:\n",
" 0 137: 1 138: 1 139: 0 140: 1 141: 1 142: 0 143: 0 144: 0 145: 1 \n",
" 146: 1 147: 1 148: 1 149: 0 150: 0 151: 0 152: 1 153: 1 154: 0 155:\n",
" 0 156: 1 157: 0 158: 0 159: 1 160: 1 161: 0 \n",
" \n",
""
]
}
}
],
"source": [
"results = design_space.run(fn_pre, fn_post, verbose=True)\n",
"df = results.to_dataframe()"
],
"id": "783dedd6"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimization and Results\n",
"\n",
"The optimization prints the best fitness achieved in after the\n",
"generation has been computed. We can further visualize this improvement\n",
"using the optimizer object stored in the `results`. After the\n",
"optimization is complete, we see that we achieved the best fitness\n",
"(reflection) of 0.897."
],
"id": "3fe73ce5-f8fb-4b32-aeaf-4d8df45c7724"
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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8eXKR6wEAAADKkjd+/ljJ6anW7aqV/PRU/3tMrAgAYAaX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}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fitness value of the best solution = 0.872"
]
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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rzZo16u/vV29vrxobG3XTTTfJYrFoxYoVuuKKK4Ydf9dddwUCi+uvv147duyQ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\nHs8995wkKSsrSyeffPKI+11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DQike68kpImHTpk2b9Ic/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\nT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}
}
],
"source": [
"# Plotting the best fitness over generations\n",
"ga_instance = results.optimizer\n",
"best_fitness = ga_instance.best_solutions_fitness\n",
"generations = range(len(best_fitness))\n",
"\n",
"plt.plot(generations, best_fitness)\n",
"plt.title(\"Best Fitness over Generations\")\n",
"plt.xlabel(\"Generation\")\n",
"plt.ylabel(\"Best Fitness\")\n",
"plt.show()\n",
"\n",
"pixels_final, solution_fitness, solution_idx = ga_instance.best_solution()\n",
"print(f\"Fitness value of the best solution = {solution_fitness:.3f}\")\n",
"\n",
"\n",
"def plot_fitness_evolution(data_frame, solutions_per_pop, fitness_column_name=\"output\"):\n",
" \"\"\"Plot the best, worst, and average fitness across generations.\"\"\"\n",
"\n",
" # Divide the solutions into generations\n",
" fitness_values = data_frame[fitness_column_name]\n",
" generation_fitness = [\n",
" fitness_values[i : i + solutions_per_pop]\n",
" for i in range(0, len(fitness_values), solutions_per_pop)\n",
" ]\n",
"\n",
" # Get min, max, and mean values per generation\n",
" min_fitness = [generation.min() for generation in generation_fitness]\n",
" max_fitness = [generation.max() for generation in generation_fitness]\n",
" mean_fitness = [generation.mean() for generation in generation_fitness]\n",
"\n",
" plt.plot(min_fitness, label=\"Worst fitness\", alpha=0.5)\n",
" plt.plot(max_fitness, label=\"Best fitness\", alpha=0.5)\n",
" plt.plot(mean_fitness, label=\"Average fitness\", alpha=0.5)\n",
" plt.xlabel(\"Generation\")\n",
" plt.ylabel(\"Fitness\")\n",
" plt.legend()\n",
" plt.show()\n",
"\n",
"\n",
"plot_fitness_evolution(df, 40)"
],
"id": "d1f1f516"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have an optimized design, we will grab the best pixel array\n",
"and re-simulate the design with a broadband\n",
"[ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html)\n",
"and a\n",
"[FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html)\n",
"at the central wavelength. This final simulation can be defined by\n",
"copying the original simulation and updating the monitors."
],
"id": "12076689-eae2-4a8e-98f9-7db080a01343"
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:02 UTC Created task 'final design' with resource_id \n",
" 'fdve-3e931e44-db29-4532-ad4a-6a1c802e3def' and task_type 'FDTD'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" View task using web UI at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e931e44-db2\n",
" 9-4532-ad4a-6a1c802e3def'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" Task folder: 'default'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:04 UTC Estimated FlexCredit cost: 0.025. This assumes the FDTD solver runs\n",
" for the full simulation time; if early shutoff is reached, the \n",
" billed cost can be lower. Use 'web.real_cost(task_id)' to get the \n",
" billed FlexCredit cost after a simulation run. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:05 UTC status = queued \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" To cancel the simulation, use 'web.abort(task_id)' or \n",
" 'web.delete(task_id)' or abort/delete the task in the web UI. \n",
" Terminating the Python script will not stop the job running on the \n",
" cloud. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:13 UTC status = preprocess \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:18 UTC starting up solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" running solver \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:19 UTC early shutoff detected at 41%, exiting. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
" status = postprocess \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:22 UTC status = success \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:24 UTC View simulation result at \n",
" 'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3e931e44-db2\n",
" 9-4532-ad4a-6a1c802e3def'. \n",
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
""
]
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/html": [
"05:00:25 UTC Loading results from simulation_data.hdf5 \n",
""
]
}
}
],
"source": [
"sim_final = make_sim(pixels_final)\n",
"\n",
"# Update mode monitor's recording frequencies\n",
"mode_monitor = sim.monitors[0].copy(update={\"freqs\": freqs})\n",
"\n",
"# Add a field monitor to visualize field distribution\n",
"field_monitor = td.FieldMonitor(\n",
" center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
")\n",
"\n",
"# Update simulation to use the new monitors\n",
"sim_final = sim_final.copy(update={\"monitors\": [mode_monitor, field_monitor]})\n",
"\n",
"# Submit the simulation to the server\n",
"sim_data_final = web.run(simulation=sim_final, task_name=\"final design\")"
],
"id": "1181d685"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the simulation, we visualize the electric field norm at the\n",
"central wavelength. As expected, a strong reflection is observed."
],
"id": "6fcd9db9-9494-45b1-ae36-dfe2956e124e"
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
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VZLydgv5wGSM0ygwj8X98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WcZzk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\nYkdJ6vIl0IwEk68n/77lev1kS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}
}
],
"source": [
"sim_data_final.plot_field(\"field\", \"Ey\", \"abs\", vmin=0, vmax=1e2)\n",
"plt.show()"
],
"id": "66e64a44"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally plot the reflection spectrum, where we see a broadband\n",
"reflection. Considering the very compact footprint, the performance is\n",
"very good. With this reflector design, we can further construct other\n",
"devices such as high-quality-factor Fabry–Perot cavities as demonstrated\n",
"in the [publication](https://doi.org/10.1364/PRJ.5.000B15)."
],
"id": "19a9ba8e-d585-4574-8bf0-5063caa560c9"
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"metadata": {},
"data": {
"image/png": 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bb799Xn311QwfPjzPP/98ujTjaS11M8saMmXKFLPCAACA1tG5c3LAAeVt9uxk\nxIjyovn//Gd5rbCmePzx8vatbyUf/3hy7LHJJz7RvMX6AZZBoWaE9enTJ0kyceLEJbZb+HjdOc1x\n8cUXp1QqpWPHjrnwwgsXCcHqrLbaavnBD36QJHnttddyww03NGuMPn36NLr1bs5UZQAAgGXVpUsy\nfHjy17+W1xS79trkf/6nHJY1RamU3HVXctxxyRprlGeZ3Xln8v/v5gFoaYUKwoYMGZIkGTduXEpL\nWKhx7Nixi53THI8++mj9uf369Wu03a677rrYOQAAACulrl2Tww5Lrr++fPvkH/+Y7LFH0sDEgAbN\nnJlccUV5kf3+/ZNvfjN56qnWrRkonEIFYVtttVWSZMaMGXnmmWcabTdq1KjFzmmOefPmJUlqamqW\n2G7hWyHrzgEAAFjp9e6dfPazyb/+VV4H7Oc/T7bbrunnv/lmeQ2xLbYoryd24YXlcA1gORUqCDvo\noIPq92+88cZG29Xdpti/f/9sueWWzR5nnXXWSZK8/PLLSwy4nn322fr9tddeu9njAAAArPDWXjv5\n6leTUaOSF19Mvv/9ZPDgpp//+OPJN76RrLtucsgh5bXITCQAllGhgrAtt9wyQ4cOTZL88pe/bHDB\n+REjRtTPCDvxxBOXaZw99tgjSfL+++/n97//fYNtSqVSfvrTny52DgAAQLs1eHBy9tnJCy8kDz6Y\nfOlLSd++TTt3/vzkxhvLa5Ktu25y2mnJEu70AWhIoYKwJPnpT3+ajh075u23384BBxyQl156qf7Y\nLbfckk9+8pNJyrPBvva1rzXYx/7775+NN964PlT7sC996Uvp1atXkuTrX/96fvazn2XGjBn1x8eN\nG5dPfepTufXWW5Mk++67b7bZZpsW+X4AAAArvKqqZPvtk//7v2TChOQf/ygvst+pU9POf+ed5Gc/\nSzbbrNzPb3+bTJnSqiUD7UNVaUmrxrdTv/vd7/LFL34xtbW1qaqqyjrrrJMZM2Zk8uTJScpPdLz7\n7ruz+eabN3j+xhtvnBdeeCG9evXKlEb+ZXv33XfnE5/4RKZOnZok6dSpU9ZZZ53MnTs3b775Zn27\nrbfeOnfeeWdWXXXVFvt+kydPTt///7cqkyZNWqYnXwIAAFTcxInJNdckl11WnjHWHF26JJ/4RHlt\nso99LOlQuHkfsMyKlCMU8t8MJ598cu67777su+++6dixY8aPH5/Jkyend+/e+dznPpenn3660RCs\nqT7+8Y/nqaeeyqmnnpr11lsv8+bNy9ixY/Pmm2+mY8eO2XrrrXPRRRflwQcfbNEQDAAAYKW16qrJ\nF76QPPBAeT2xM88sP0GyKWbPTv72t2TPPZONNkrOPz95663WrRdY6RRyRtjC5s2blwkTJqSmpib9\n+vVLVRMe7TtmzJjMmTMnHTt2zOAmLvI4ZcqUTJo0KdXV1enXr98iT4xsaUVKcgEAgHautja5557k\n0kuT668vB15NVV2dHHRQ8rnPJXvvnXTs2Hp1wkqsSDlC4YOw9qhIFzAAAFAgU6YkV19dDsX+/0PO\nmqx///Jtk5/9bLLeeq1SHqysipQjFPLWSAAAAFZCvXsnJ5+cPPRQ8vTT5SdH9uvXtHPHjUu+//1k\n/fWTAw8sP4Fy/vxWLBZYEQnCAAAAWPlsumlywQXJ+PHlUGv48Kbd+lhbm9xyS3LIIckGGyTnnJMs\n9EAzoH0ThAEAALDy6tQpOfjg5IYbyrO+fvSjcsDVFOPHJ2efnQwYkBx+eHL33YnVg6BdE4QBAADQ\nPqy9dnLGGcnLLyd33FEOtzp1Wvp58+cnf/97ssceyUc+klx0UTJ5cquXC1SeIAwAAID2pUOHZK+9\nkmuuKc/6uuCCZKONmnbuCy8kX/tass465YX1H364dWsFKkoQBgAAQPvVr195Uf3nn0/+/e/kU59K\nOnde+nmzZiV/+lMydGh5u/zyZM6cVi8XaF2CMAAAANq/qqpk992TK65I3ngj+clPkoEDm3buww8n\nxx6b9O+fnHWWxfVhJSYIAwAAoFhWWy05/fTkpZeS224rL7bfoQn/9/idd5L//d/y4vpHH52MHGlx\nfVjJCMIAAAAopg4dkn33TW68MRkzJjnzzGTNNZd+3vz5yVVXJTvvnGy7bfLnPyezZ7d6ucDyE4QB\nAABA//7l2V7jxpUX2f/Yx5p23qOPJp/5TLLeeuUgzW2TsEIThAEAAECdTp2Sww9P7r47efrp5POf\nT7p1W/p5772X/OhHyfrrl9cTe+yxVi8VaD5BGAAAADRk002T3/ymvLj+hRc2bXH9efPKT5jcZpvy\nrLKbbkpqa1u/VqBJBGEAAACwJL17J1/7Wnlx/ZtvTvbeu2nn/fvf5YX4N944+fWvkxkzWrNKoAkE\nYQAAANAUHTokBxyQjBiRPP988uUvJz16LP28l15KvvSl8jpi3/lOeYYZ0CYEYQAAANBcQ4Ykv/xl\nOdS66KJkgw2Wfs7kycn553+wjthTT7V2lcCHCMIAAABgWfXsmXzlK+VZX9dfn+yyy9LPmT+/vI7Y\nFluUZ5jde29SKrV+rYAgDAAAAJZbx47J//xPct99yahRydFHlz9bmltvTYYNS3bYIbnuumTBglYv\nFYpMEAYAAAAtabvtkr/9LRkzJvnmN8uL7S/NqFHJYYclH/lI8vvfJ7Nnt3qZUESCMAAAAGgN662X\n/PjHyeuvl9cT23DDpZ/z0kvJyScnAwYk555bXlcMaDGCMAAAAGhNPXqUnzD5wgvl2x+HDl36Oe+8\nk3z3u0n//sm3vpW8/Xbr1wkFIAgDAACASujYMfnEJ5IHH0z+/e9k//2Xfs706clPflJ+0uQppyTj\nxrV2ldCuCcIAAACgkqqqkt13T265JXnyyeTYY5Pq6iWfM3t28n//V7698rOfTV58sTK1QjsjCAMA\nAIC2svnmyV/+krzySvK1ryXduy+5/fz5yZ/+VF5U/6ijykEa0GSCMAAAAGhr/fsnF15YXlj/Rz9K\nVl99ye1ra5Orr0623DI5+ODkoYcqUyes5ARhAAAAsKLo0yc544xk7Njk4ouTdddd+jk33ZTssEOy\n777JAw+0eomwMhOEAQAAwIqmW7fy4vivvJL84Q/JoEFLP2fEiGSnnQRisASCMAAAAFhRde6cnHBC\n8txzyd/+lmy22dLPEYhBowRhAAAAsKKrrk6OPjp54onkhhuS7bZb+jkCMViMIAwAAABWFh06JMOH\nlxfHv+OOZLfdln6OQAzqCcIAAABgZVNVley1V3Lvvck99yTDhi39nIUDsdGjW71EWBEJwgAAAGBl\nNmxYOQxrTiC27bbJoYcmzzzT2tXBCkUQBgAAAO1BcwOx669PNt88OfbY5NVXW7s6WCEIwgAAAKA9\naU4gVioll1+eDBmSfP7zyRtvVKJCaDOCMAAAAGiP6gKxf/976YHY/PnJ736XbLhh8o1vJO++W4EC\nofIEYQAAANCe7b57ORC7665khx2W3HbOnOTCC5OBA5PvfS+ZOrUyNUKFCMIAAACgCD7+8WTkyOSm\nm5Ittlhy2+nTkx/+MBk0KPnFL8oBGbQDgjAAAAAoiqqq5MADk8ceS666KtlooyW3f++95KtfTT7y\nkeTKK5Pa2oqUCa1FEAYAAABF06FDcuSRyTPPJJdemgwYsOT2Y8Ykn/xkst12yb/+VZkaoRUIwgAA\nAKCoqquTz3wmeeGF5Je/TNZcc8ntH3002WuvZJ99kscfr0iJ0JIEYQAAAFB0NTXJl7+cvPJKct55\nSa9eS25/xx3JNtskn/50MnZsRUqEliAIAwAAAMq6dUu+/e1yIPb1ryedOzfetlRKrrgiGTIkOe20\nZMqUipUJy0oQBgAAACxq1VWTn/2sfMvkMceUF9lvzNy55baDBiW/+lUyf37l6oRmEoQBAAAADVt/\n/eTyy8trg+2995LbTpxYvr1yiy2S226rSHnQXIIwAAAAYMm22ioZMSK5885k662X3Pa555L990/2\n3Td5+umKlAdNJQgDAAAAmmbPPZNHHkn+9rdkgw2W3HbEiGTLLZMvfCF5553K1AdLIQgDAAAAmq5D\nh+Too8szvy64IOnZs/G2tbXJb3+bDB6c/OQnyZw5lasTGiAIAwAAAJqvpqb8tMiXX06++MWkY8fG\n206blnzrW8lHPpLccEP5iZPQBgRhAAAAwLJbffXy0yKffDLZb78ltx0zJvmf/ymvH/b885WpDxYi\nCAMAAACW3yabJLfemtx+e3l/Se64I9l88+T008uzxaBCBGEAAABAy9lnn+SJJ5Lf/CZZbbXG282f\nn/z0p8mQIclll5XXE4NWJggDAAAAWlZ1dfL5z5fXDzv99KRz58bbvvVWctxxyS67JI8+WrkaKSRB\nGAAAANA6evUqPy3y6aeTAw5YctsHHki23TY5+eTkvfcqUx+FIwgDAAAAWtfgwcnNN5e3QYMab1cq\nJb//fbn9b36TLFhQuRopBEEYAAAAUBkHHFCeHXbeeUn37o23mzIl+eIXkx13dLskLUoQBgAAAFRO\nTU3y7W8nzz+fHH30kts+/HCy3XbJqacmU6dWpj7aNUEYAAAAUHnrrpv87W/JvfcmW2zReLva2uSX\nv0w23ji56qry7ZOwjARhAAAAQNvZbbdk9OjkV79K+vRpvN1bb5VnkO29d/LSS5Wrj3ZFEAYAAAC0\nrerq8ppgL7yQHH/8ktv+61/JZpslZ5+dzJ5dkfJoPwRhAAAAwIph9dWTP/0p+c9/kk03bbzd3LnJ\nOeeUA7ERIypXHys9QRgAAACwYtl11+Sxx5If/zjp1q3xdq+8kuy7b3LMMcm771auPlZagjAAAABg\nxdOpU/LNbybPPpsMH77ktn/9a/KRjyRXXGExfZZIEAYAAACsuAYMSG64IfnnP8v7jZk4Mfn0p5P9\n9kvGjq1UdaxkBGEAAADAiu+gg5Jnnkm+9a3y4vqNGTGivL7Yz3+eLFhQufpYKQjCAAAAgJVD9+7J\n+ecnjz+e7LRT4+1mzky+/vVkxx2TJ5+sWHms+ARhAAAAwMpl002T++5LfvWrpEePxts9/HDy0Y8m\n3/1uMnt25epjhSUIAwAAAFY+HTokX/xieTH9Aw9svN38+cm55yZbbFEOzyg0QRgAAACw8lpvvfJC\n+ldfnfTr13i7l15Kdt89+epXy7dOUkiCMAAAAGDlVlWVHHFE8txzyWc+03i7Uin5xS/MDiswQRgA\nAADQPvTtm1x6afKvfyUDBzbe7pVXzA4rKEEYAAAA0L7ssUfy1FPJN7+ZdOzYcBuzwwpJEAYAAAC0\nP926JT/+cfLgg+WnTDbG7LBCEYQBAAAA7de22yajRydnnGF2GIIwAAAAoJ2rqUl+9KOmzw772teS\nWbMqVx8VIwgDAAAAiqGps8Muuij56EeTRx+taHm0PkEYAAAAUBxNnR323HPJ9tuX286fX7n6aFWC\nMAAAAKB4mjI7bP785Mwzk912S15+ubL10SoEYQAAAEAxNXV22AMPJFttlfz+9+VbJ1lpCcIAAACA\nYtt22+SRR5LTTkuqqhpuM2NGcvLJyUEHJW+9Vdn6aDGCMAAAAIAuXZILLkjuuSfp37/xdrfckmy2\nWXL99ZWrjRYjCAMAAACos/vuyZNPJscd13ibiROTQw9NPvOZZNq0ytXGchOEAQAAACysV6/kz39O\nrrsuWXXVxtv9+c/J1lsno0ZVqjKWkyAMAAAAoCGf+ETy9NPJ/vs33ubVV5Odd07OOy9ZsKBytbFM\nBGEAAAAAjVlzzeTmm5Pf/S7p1q3hNvPnJ2eckey1V/LGG5Wtj2YRhAEAAAAsSVVVctJJyRNPJDvs\n0Hi7e+5JttwyufHGytVGswjCAAAAAJpi0KDkvvuSs89OOjQSqUycmBxySPKlLyWzZlW0PJZOEAYA\nAADQVNXVyfe/n/z738l66zXe7te/ToYOLa8xxgpDEAYAAADQXLvuWr5V8rDDGm/z9NPJttsmv/pV\nUipVrjYaJQgDAAAAWBZ9+iTXXJP84Q+NL6Q/Z07y5S8n//M/yeTJla2PxQjCAAAAAJZVVVVywgnJ\n6NHJVls13u7GG5Ott04eeqhipbE4QRgAAADA8tp44+TBB5Ovfa3xNq+9Vr6l8qKL3CrZRgRhAAAA\nAC2hpia58MLkttuSfv0abjNvXjksc6tkmxCEAQAAALSkffdNnnwy2XvvxtvU3So5alTl6kIQBgAA\nANDi1lijPDPsRz9KOjQSv7z2WrLLLm6VrCBBGAAAAEBr6NAhOeOM5J57krXWarhN3a2Sn/iEWyUr\nQBAGAAAA0Jp22y15/PEl3yp5ww3JNtu4VbKVCcIAAAAAWlu/fuVbJX/4w8ZvlRw7tnyr5K9/7VbJ\nViIIAwAAAKiEDh2S7343ufvuJd8q+aUvJccem8ycWdn6CkAQBgAAAFBJu+9evlVyr70ab3PFFcmO\nOyYvv1yxsopAEAYAAABQaf36JbffvuRbJZ98Mtl22+SmmypbWzsmCAMAAABoC3W3St51VzkYa8jU\nqcnBBydnnpksWFDZ+tohQRgAAABAWxo2LHn00WSnnRpv86MfJfvtl7z3XsXKao8EYQAAAABtbZ11\nknvuSU45pfE2d96ZfPSjycMPV66udkYQBgAAALAi6Nw5ufji5K9/Tbp1a7jNuHHJLrskl1ySlEqV\nra8dEIQBAAAArEg++cnkwQeTQYMaPj53bnLSScnnPpfMmVPZ2lZygjAAAACAFc3mmyePPJIMH954\nmz/+sby+2JtvVqyslZ0gDAAAAGBF1KtXcv31yXnnlZ8w2ZAHH0y23TZ56KHK1raSEoQBAAAArKg6\ndEi+/e3kjjuS1VZruM2ECcluuyV/+lNla1sJCcIAAAAAVnR77JE8+mh59ldD5s5NPvvZ5NRTk3nz\nKlvbSkQQBgAAALAyWG+95D//ST796cbb/PKXyT77JO+9V7m6ViKCMAAAAICVRdeuyV/+kvz8542v\nG3bPPeWZY48/XtHSVgaCMAAAAICVSVVV8tWvJiNGJH37NtzmtdeSnXZKrr66oqWt6ARhAAAAACuj\nPfdMHn442Wyzho/PmpUcdVTyne8kCxZUtrYVlCAMAAAAYGU1cGDywAPJoYc23ub885NPfCKZPr1y\nda2gBGEAAAAAK7MePZJrr03+938bb/PPfya77JKMG1e5ulZAgjAAAACAlV1VVXLmmeXAa5VVGm7z\nxBPJ0KHJgw9WtrYViCAMAAAAoL046KDkoYeSwYMbPv7228mwYcmVV1a0rBWFIAwAAACgPfnIR8ph\n2B57NHx8zpzkk59Mzjorqa2tbG1tTBAGAAAA0N706ZPcdlvy+c833uZ//7f8VMmZMytXVxsThAEA\nAAC0R506Jb/+dXLxxUmHRiKga69NDjywsnW1IUEYAAAAQHtVVZWcckpyyy1Jz54Nt3n88YqW1JYE\nYQAAAADt3b77Jg88kAwc2NaVtClBGAAAAEARbLJJeRH9XXdt60rajCAMAAAAoChWWy3517+Sz3ym\nrStpE4IwAAAAgCLp3Dn54x+TCy4oryFWIIIwAAAAgKKpqkpOOy254YakW7e2rqZiBGEAAAAARXXw\nwcmIEW1dRcUUNggbN25cvv71r2fIkCHp3r17+vbtm+233z4///nPM2fOnBYda8KECTnnnHMydOjQ\nrL766unatWs22mijHHroobnmmmsya9asFh0PAAAAoMk23bStK6iYqlKpVGrrIirt9ttvz5FHHplp\n06Y1eHyzzTbLHXfckbXWWmu5x/rLX/6SU089tdGxkuS8887Lt7/97eUeq87kyZPTt2/fJMmkSZPS\np0+fFusbAAAAaF+KlCMUbkbYs88+m8MOOyzTpk3LgAEDcvPNN+f999/PW2+9lQsuuCCdOnXK008/\nnQMPPDDz589frrF++9vf5vjjj8+0adOyzTbb5Nprr83bb7+duXPn5q233sqNN96Yo446Kt27d2+h\nbwcAAABAYwo3I+yAAw7Irbfemu7du+epp57KBhtssMjxSy+9NCeccEKS5De/+U0+//nPL9M4Tz/9\ndLbZZpvMmzcvhxxySK699tpUV1cvd/1NUaQkFwAAAFg+RcoRCjUj7NVXX82tt96aJPnCF76wWAiW\nJJ/97GczZMiQJMkvf/nLZR7rG9/4RubNm5d+/frlz3/+c8VCMAAAAAAaVqgg7MYbb6zfP+KIIxpt\nd/jhhycp30b50ksvNXucV155JXfeeWeS5KSTTkqvXr2a3QcAAAAALatQQdjDDz+cJKmpqcnWW2/d\naLsdd9yxfv+RRx5p9ji33XZb6u44HT58eLPPBwAAAKDlVSQIe/PNNysxzFI9//zzSZL+/fsv8VbF\ngQMHLnZOc4waNSpJ0rlz52y55Za57777cuihh2bNNddMTU1N1lprrQwfPnyRGWoAAAAAtK6KLFw1\nfPjwdOrUKSeccEKOPPLI9OjRoxLDLua9995LkvTr12+J7RY+XndOc4wbNy5Jsvbaa+fHP/5xzjrr\nrCz8TIK33nor//znP/PPf/4zn/jEJ/LXv/41Xbp0adYYkydPbvTYlClTml0zAAAAQHtXkRlhpVIp\nDzzwQE488cSstdZaOfHEE/PAAw9UYuhFTJ8+PUmWGjp17dp1sXOaoy6ImjBhQr73ve+lX79+ueyy\nyzJx4sTMmjUrI0eOzMc+9rEkyfXXX59TTjml2WP07du30W3hGW0AAAAAlFUkCPvGN76RbbfdNkk5\nWPrjH/+YnXbaKZtuumkuvPDCvPvuu5Uoo15VVVWr9l9bW5skmTNnTrp06ZK77747n/70p9O3b990\n6dIlO+64Y0aMGJGddtopSfLHP/4xzzzzTKvWBAAAAFB0FQnCjj766Dz88MN57LHH8sUvfrH+KYrP\nPvtsvvGNb2TdddfN4Ycfnttvv70+RGoNdbdkzpo1a4ntFj6+LLdxLnzOpz71qWyyySaLtenUqVO+\n//3vJynPmLvuuuuaNcakSZMa3V599dVm1wwAAADQ3lX0qZFbbbVVfvWrX2XChAn585//nF122SVJ\nMnfu3Pz973/Pfvvtl/XXXz9nn312xo4d2+Ljr7baakmSd955Z4ntFj5ed05zrL766vX7u+++e6Pt\ndtttt/rZaU8++WSzxujTp0+jW+/evZtdMwAAAEB7V9EgrE7Xrl1z3HHH5b777suzzz6br3/96/WB\n0+uvv55zzjknAwcOzN57752rr746c+bMaZFxN9544yTlxeznz5/faLuFZ1TVndMcH/nIR+r3+/Tp\n02i7mpqadOvWLUkybdq0Zo8DAAAAQNO1SRC2sI985CP52c9+ljfeeCNXXXVV/QyqUqmUO++8M0cd\ndVTWXnvtnH766fVPY1xW2223XZLy2l2PPfZYo+0WXsi/bm2z5hg6dGj9/pKe7jh79uzMnDkzSczi\nAgAAAGhlbR6E1Rk3blwef/zxvPDCC4sdmzRpUn76059m8ODB+e53v5sFCxYs0xjDhw+v37/66qsb\nbXfttdcmSTbZZJMMHjy42ePss88+9TO97r333kbb3XfffSmVSkmSrbfeutnjAAAAANB0bRqEzZkz\nJ1deeWU+/vGPZ6ONNsr555+ft956K1VVVdljjz1yzTXX5NZbb81hhx2Wzp07Z+7cuTn33HNzxhln\nLNN4AwcOzP77758k+d3vfpcxY8Ys1ubSSy+tD+NOOeWUZRqne/fu+dznPpck+etf/5pnn312sTbz\n58+vXyy/uro6RxxxxDKNBQAAAEDTVJXqpiRV0NNPP50//OEPufzyyzNp0qT6z1dfffUcf/zxOemk\nkzJo0KBFzpkwYUJOPfXU/P3vf0+XLl0yZcqU1NTUNHvsZ599NkOHDs2MGTMyYMCA/OpXv8qwYcMy\nY8aMXH755fnOd76TefPmZZtttslDDz2U6urqxfrYeOON88ILL6RXr16ZMmVKg+NMnDgxW2+9dV5/\n/fWsscYa+elPf5oDDjgg3bp1y6OPPprvfve7ueeee5Ik3/72t3Peeec1+7s0ZvLkyenbt2+S8my6\nJa1TBgAAABRbkXKEigVhM2bMyNVXX51LLrkkDz744CLHdt9995x88sk59NBD07lz50b7mD17dtZa\na61MmTIlzz333DItZJ8kt99+e4488shGF6jfbLPNcscdd2SttdZq8HhTgrCkHLodcMABS3wC5he/\n+MX88pe/TIcOLTc5r0gXMAAAALB8ipQjLD7dqRWcddZZueiii/L+++/Xf9a3b98cd9xxOfnkkzNk\nyJAm9dOlS5dssMEGeeyxxzJ9+vRlrmfffffNU089lZ///Oe59dZb8/rrr6empiYbbbRRjjzyyHzp\nS19aptlmH7bJJpvkqaeeyv/93//l+uuvz0svvZSZM2dmjTXWyC677JIvfOEL2XXXXZd7HAAAAACW\nriIzwrbddtuMHj06SbLzzjvn5JNPzuGHH54uXbo0u6+TTz45L7zwQn73u981OUArmiIluQAAAMDy\nKVKOUJEZYX369Mkpp5ySk08+OZtuuuly9fW73/2uhaoCAAAAoEgqEoSNGDGiRdfAAgAAAIDmqkg6\nNWfOnEyfPj21tbVNPmfWrFnNPgcAAAAAGlORIGzXXXfNKquskkcffbRVzwEAAACAxrhfEQAAAIBC\nWGGDsLpbIjt27NjGlQAAAADQHqywQdiECROSJKusskobVwIAAABAe9AqT42cNWtWFixYUP++bnZX\n3QL4jamtrc3EiRNz2WWX5a233krnzp0zYMCA1igRAAAAgIJplSBs1113zejRoxf7fLfddmtWP8OH\nD0+nTp1aqiwAAAAACmyFvTVyl112yS9+8Yu2LgMAAACAdqJVZoR973vfy7vvvlv//oc//GFee+21\nnHnmmUu81bFjx47p1atXNt988wwePLg1SgMAAACgoFolCBs+fPgi73/729/mtddey/Dhw7Ptttu2\nxpAAAAAAsEStEoR92NVXX51Zs2Zl0KBBlRgOAAAAABZTkSBsww03rMQwAAAAANCoFXaxfAAAAABo\nSS06I2zq1KmZN29ekqR3796prq5e7PPmWrgfAAAAAFhWLZow7bHHHhk9enSS5OGHH65fGH/hz5tr\n4X4AAAAAYFm5NRIAAACAQmjRGWHnnntuJk2alGTRBfIX/ry5LLQPAAAAQEto0SBs7733btbnAAAA\nAFApbo0EAAAAoBAqEoS98cYblRgGAAAAABpVkSDskEMOyUc/+tFcfPHFeffddysxJAAAAAAsoiJB\nWKlUyqOPPpqvfOUrWWeddXLwwQfnuuuuy9y5cysxPAAAAABUJgg7/fTTs+uuu6aqqirz5s3LTTfd\nlMMOOyxrrrlmvvjFL+bBBx+sRBkAAAAAFFhVqVQqVWqwMWPG5PLLL8/ll1+el19+eZFjG220UY49\n9th8+tOfTv/+/StVUrs0efLk9O3bN0kyadKk9OnTp40rAgAAAFZURcoRKhqELWzkyJG57LLLcvXV\nV2fKlCkfFFRVlWHDhuXYY4/NYYcdlh49erRFeSu1Il3AAAAAwPIpUo7QZkFYnTlz5uSf//xnLrvs\nstx+++2ZP39+/bFu3bpl9OjR2XjjjduwwpVPkS5gAAAAYPkUKUeoyBphS1JTU5PDDz88N910U958\n881cdNFF2XzzzZMkM2fOzPTp09u4QgAAAADagzYPwuqUSqU888wzeeKJJzJ27Ni2LgcAAACAdqa6\nrQt44YUXctlll+WKK67IuHHjFjm21VZbpV+/fm1UGQAAAADtSZsEYe+9916uuuqqXH755Rk1atQi\nx9Zcc8186lOfynHHHVd/iyQAAAAALK+KBWFz5szJzTffnMsvvzy33npr5s2bV3+sS5cuGT58eI47\n7rjsvffe6dixY6XKAgAAAKAgKhKEff/738/FF1+cyZMnL/L5zjvvnOOOOy5HHHFEevXqVYlSAAAA\nACioigRhN998c30ItsEGG+TTn/50jj322Gy44YaVGB4AAAAAKhOE9ezZM5/97Gdz7LHHZrfddktV\nVVUlhgUAAACAehUJwu68807rfgEAAADQpjpUYhAhGAAAAABtrSJBGAAAAAC0tRa9NXLq1KmZN29e\nkqR3796prq5e7PPmWrgfAAAAAFhWLZow7bHHHhk9enSS5OGHH86222672OfNtXA/AAAAALCs3BoJ\nAAAAQCG06Iywc889N5MmTUqSbLjhhg1+3lwL9wMAAAAAy6pFg7C99967WZ8DAAAAQKW4NRIAAACA\nQqhIEDZr1qxMnz49tbW1rXoOAAAAADSmIkHYrrvumlVWWSWPPvpoq54DAAAAAI1xayQAAAAAhbDC\nBmF1t0R27NixjSsBAAAAoD1YYYOwCRMmJElWWWWVNq4EAAAAgPagujU6nTVrVhYsWFD/vm52V90C\n+I2pra3NxIkTc9lll+Wtt95K586dM2DAgNYoEQAAAICCaZUgbNddd83o0aMX+3y33XZrVj/Dhw9P\np06dWqosAAAAAApshb01cpdddskvfvGLti4DAAAAgHaiVWaEfe9738u7775b//6HP/xhXnvttZx5\n5plLvNWxY8eO6dWrVzbffPMMHjy4NUoDAAAAoKBaJQgbPnz4Iu9/+9vf5rXXXsvw4cOz7bbbtsaQ\nAAAAALBErRKEfdjVV1+dWbNmZdCgQZUYDgAAAAAWU5EgbMMNN6zEMAAAAADQqIotln/ffffl9ttv\nz8yZM5fa9uGHH87tt9+eSZMmVaAyAAAAAIqgIkHY22+/nY997GP5zGc+k5qamqW2/+c//5n99tsv\nv/3tbytQHQAAAABFUJEg7KqrrsqCBQty1FFHpWPHjkttf8wxxyRJ/va3v7V2aQAAAAAUREWCsEcf\nfTRJss022zSp/ZAhQ9KtW7c8++yzmTNnTmuWBgAAAEBBVCQIe/3115Mk/fv3b/I56623XkqlUt54\n443WKgsAAACAAqlIEDZ//vwkSalUavI5dW1nzZrVKjUBAAAAUCwVCcLWWGONJMmTTz7ZpPbvv/9+\nxowZkyTp169fq9UFAAAAQHFUJAjbcccdkySXXHJJFixYsNT2l1xySebNm5cNN9wwq6++emuXBwAA\nAEABVCQIO+KII9K5c+c8/fTTOeGEEzJ37txG2956660544wzknzw9EgAAAAAWF5VpeYs3LUczjjj\njJx33nlJkkGDBuXEE0/MDjvskFVXXTWzZ8/O888/n+uuuy433nhjSqVSNthggzz22GPp1atXJcpr\nVyZPnpy+ffsmSSZNmpQ+ffq0cUUAAADAiqpIOULFgrAFCxbk2GOPzd/+9reltu3fv39uu+22bLLJ\nJhWorP0p0gUMAAAALJ8i5QgVuTUySTp27Ji//vWv+dOf/pTBgwc32KZ79+758pe/nNGjRwvBAAAA\nAGhRFZsR9mEvvfRSnn766UyePDldu3ZN//79s91226Vz585tUU67UqQkFwAAAFg+RcoRqttq4MGD\nBzc6MwwAAAAAWlrFbo0EAAAAgLbUJjPCSqVSXnzxxbz99tuZOXNmdtppp/Ts2bMtSgEAAACgICo6\nI2zq1Kn51re+ldVXXz0bb7xxdt999+y333558cUXF2n3gx/8IPvuu28eeuihSpYHAAAAQDtWsRlh\n48aNyx577JGXX355qW179OiRESNGZMstt8z2229fgeoAAAAAaO8qMiOsVCrl0EMPzcsvv5xevXrl\nnHPOyYMPPpjNNtuswfb7779/kuSOO+6oRHkAAAAAFEBFZoTdeOONeeSRR9KzZ8+MHDkym2yySZKk\npqamwfYbb7xxqqur89RTT2Xu3Lnp3LlzJcoEAAAAoB2ryIywW265JUnyla98pT4ES5KqqqoG21dV\nVWX11VfPggUL8vrrr1eiRAAAAADauYoEYa+88kqSZNiwYU0+Z7XVVkuSTJ8+vTVKAgAAAKBgKhKE\nzZ07N0nSpUuXRT5vbEZY8kEA1q1bt9YrDAAAAIDCqEgQ1q9fvyTJmDFjmtR+0qRJGTt2bDp06JA1\n11yzNUsDAAAAoCAqEoTtsMMOSZKrrrpqkc8bmxH2m9/8JqVSKVtuuWVWWWWVVq8PAAAAgPavIkHY\nkUcemc6dO+fmm2/OJZdcssS2t912W84555wkyWc/+9lKlAcAAABAAVQkCBswYEBOP/30JMlJJ52U\nQw45JNdee21mzJiRJHn55Zdz7bXX5vDDD88BBxyQuXPnZvPNN8+JJ55YifIAAAAAKICqUqlUqsRA\ntbW1OfHEE/OnP/1pqW0HDRqUESNGZODAgRWorP2ZPHly+vbtm6S83lqfPn3auCIAAABgRVWkHKEi\nM8KSpEOHDrn00ktz1VVXZYsttmiwTbdu3XLqqadm1KhRQjAAAAAAWlTFZoR92GuvvZYnnngiEydO\nTE1NTdZbb70MHTo0NTU1bVFOu1KkJBcAAABYPkXKEarbauABAwZkwIABbTU8AAAAAAVTsVsjAQAA\nAKAtCcIAAAAAKIQWvTXyggsuyGuvvdaSXeab3/xm+vfv36J9AgAAAFA8LRqEXX311Rk9enRLdpnj\njz9eEAYAAADAcmuVxfLXXXfdFnv6Y5cuXVqkHwAAAACKrVWCsH/84x/ZdtttW6NrAAAAAFgmFssH\nAAAAoBAqEoRdeuml+eEPf5gJEyZUYjgAAAAAWEy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5eXzrrr169HfHw8ACA1NRUvv/yyzro//fST1O77779vxIipKoWEhODkyZNo3rw5Vq1aVd3hqElL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\nRERERERERFQn/D8++W3IF7NC5AAAAABJRU5ErkJggg==\n"
}
}
],
"source": [
"R = abs(sim_data_final[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2\n",
"\n",
"plt.plot(ldas, R, \"red\", linewidth=2)\n",
"plt.xlim(min(ldas), max(ldas))\n",
"plt.ylim(0, 1)\n",
"plt.xlabel(\"Wavelength (µm)\")\n",
"plt.ylabel(\"Reflectivity\")\n",
"plt.show()"
],
"id": "43a5b8a4"
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the optimized design, we can directly export a GDS file of the\n",
"reflector for fabrication."
],
"id": "5a83fa89-0c23-43ce-8dff-46754ee4858c"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# Make the misc/ directory to store the GDS file if it doesn't exist already\n",
"import os\n",
"\n",
"if not os.path.exists(\"./misc/\"):\n",
" os.mkdir(\"./misc/\")\n",
"\n",
"sim_final.to_gds_file(fname=\"misc/optimized_reflector.gds\", z=t / 2)"
],
"id": "08d891cb"
}
],
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
}
}