From c0d50bd7f5b552de810ac0dad2159a70925a0cf3 Mon Sep 17 00:00:00 2001 From: fyc2646 Date: Mon, 8 Dec 2025 11:33:47 -0500 Subject: [PATCH] Adding the bilayer grating coupler notebook Adding the bilayer grating coupler notebook --- BilayerSiNSiGC.ipynb | 2082 +++++++++++++++++++++++++++++++++++++ docs/case_studies/pic.rst | 1 + img/bilayer_GC.png | Bin 0 -> 167766 bytes img/bilayer_GC_2.png | Bin 0 -> 41332 bytes 4 files changed, 2083 insertions(+) create mode 100644 BilayerSiNSiGC.ipynb create mode 100644 img/bilayer_GC.png create mode 100644 img/bilayer_GC_2.png diff --git a/BilayerSiNSiGC.ipynb b/BilayerSiNSiGC.ipynb new file mode 100644 index 00000000..ff9ead7d --- /dev/null +++ b/BilayerSiNSiGC.ipynb @@ -0,0 +1,2082 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "426ed43a", + "metadata": {}, + "source": [ + "# Design optimization of a bilayer SiN/Si grating coupler\n", + "\n", + "Silicon nitride has attracted increasing interest due to its superior passive properties. However, similar to silicon PICs, fiber-to-chip coupling remains a significant challenge for SiN platforms. Conventional SiN grating couplers exhibit low coupling efficiency due to their low refractive index contrast. To address this, a high-contrast grating reflector (GR) is employed as a bottom reflector to enhance coupling efficiency, rather than using distributed Bragg reflectors (DBR) or metal reflectors. Through design optimization, combining parameter sweeps and adjoint-based inverse design, a grating coupler with high coupling efficiency (< 1 dB) is achieved.\n", + "\n", + "The workflow includes:\n", + "1. **Periodic SiN Grating Coupler Design**: Optimizing the SiN grating period, gap size, and fiber position.\n", + "2. **Silicon Grating Reflector Design**: Designing a bottom silicon grating to reflect leakage light upwards.\n", + "3. **Interlayer Distance Optimization**: Tuning the distance between the SiN and Si layers.\n", + "4. **Inverse Design**: Using adjoint optimization to apodize the grating for maximum efficiency.\n", + "\n", + "
\"schematic\"
\n", + "\n", + "The design is based on the following publication: `Jinghui Zou, Yu Yu, Mengyuan Ye, Lei Liu, Shupeng Deng, and Xinliang Zhang, \"Ultra efficient silicon nitride grating coupler with bottom grating reflector,\" Opt. Express 23, 26305-26312 (2015).` [DOI: 10.1364/OE.23.026305](https://doi.org/10.1364/OE.23.026305)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "197606b2-7f1d-41bc-9a1a-ba3a951c3745", + "metadata": {}, + "outputs": [], + "source": [ + "import autograd as ag\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import optax\n", + "import tidy3d as td\n", + "import tidy3d.plugins.design as tdd\n", + "import tidy3d.web as web" + ] + }, + { + "cell_type": "markdown", + "id": "23482b29", + "metadata": {}, + "source": [ + "## Simulation Setup\n", + "We define the central wavelength, frequency, and bandwidth for the simulation. The simulation will target 1550 nm." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1d08568d-68f7-4aab-8cfd-7b1cc976652c", + "metadata": {}, + "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\n", + "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # Frequency width of the source" + ] + }, + { + "cell_type": "markdown", + "id": "de04ab76", + "metadata": {}, + "source": [ + "We define the relevant materials as nondispersive mediums for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e4239328-c446-4a3c-aaea-aaf66901e310", + "metadata": {}, + "outputs": [], + "source": [ + "SiN = td.Medium.from_nk(n=1.97, k=0, freq=freq0)\n", + "Si = td.Medium.from_nk(n=3.47, k=0, freq=freq0)\n", + "SiO2 = td.Medium.from_nk(n=1.44, k=0, freq=freq0)" + ] + }, + { + "cell_type": "markdown", + "id": "7effc418", + "metadata": {}, + "source": [ + "Defining the fixed geometric parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8e5510a0-711b-42ff-96c7-e36ac9c2c194", + "metadata": {}, + "outputs": [], + "source": [ + "t_SiN = 0.4 # Thickness of SiN layer\n", + "t_Si = 0.22 # Thickness of Silicon waveguide\n", + "t_clad = 0.75 # Target cladding thickness\n", + "t_box = 2 # Thickness of Buried Oxide (BOX)\n", + "theta = np.deg2rad(8) # Fiber angle\n", + "mfd = 10.4 # Mode field diameter\n", + "\n", + "n_gc = 14 # Number of SiN grating teeth\n", + "inf_eff = 1e3 # Effective infinity" + ] + }, + { + "cell_type": "markdown", + "id": "e7a8582a", + "metadata": {}, + "source": [ + "Next we define some fixed simulation parameters. These will be used repeatedly in various following simulation setups." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "29fa4692-772c-47a3-870a-f094a068a0ce", + "metadata": {}, + "outputs": [], + "source": [ + "run_time = 3e-12 # Simulation run time\n", + "\n", + "# Grid specification\n", + "grid_spec = td.GridSpec.auto(min_steps_per_wvl=20)\n", + "\n", + "# Boundary condition specification\n", + "boundary_spec = td.BoundarySpec(\n", + " x=td.Boundary.absorber(num_layers=80),\n", + " y=td.Boundary.periodic(), # set the boundary to periodic in y since it's a 2D simulation\n", + " z=td.Boundary.pml(),\n", + ")\n", + "\n", + "# Mode monitor for coupling efficiency measurement\n", + "mode_monitor = td.ModeMonitor(\n", + " center=(-lda0 / 2, 0, t_SiN / 2),\n", + " size=(0, td.inf, 5 * t_SiN),\n", + " freqs=freqs,\n", + " mode_spec=td.ModeSpec(num_modes=1, target_neff=1.97),\n", + " name=\"mode\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f4730455", + "metadata": {}, + "source": [ + "## Periodic SiN Grating Coupler Design\n", + "\n", + "In the first part, we design and optimize a periodic SiN grating coupler by parameter sweeping the grating period, duty cycle, and fiber position. \n", + "\n", + "Since we are optimizing the grating only, we will ignore the silicon layer as well as the substrate for now. They will be added and optimized later.\n", + "\n", + "
\"schematic\"
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c72196ee-4c77-431f-aecf-b0baf2db7e28", + "metadata": {}, + "outputs": [], + "source": [ + "def make_2D_SiN_grating(w_gc: float, p_gc: float) -> td.Structure:\n", + " \"\"\"\n", + " Creates a 2D SiN grating structure.\n", + "\n", + " Parameters:\n", + " w_gc (float): Width of the etched region.\n", + " p_gc (float): Period of the grating.\n", + "\n", + " Returns:\n", + " td.Structure: The resulting grating structure.\n", + " \"\"\"\n", + " gratings = 0\n", + " # Iterate to create each period of the grating\n", + " for i in range(n_gc):\n", + " # Add a box for each grating period\n", + " gratings += td.Box.from_bounds(\n", + " rmin=(w_gc + i * p_gc, -inf_eff, 0), rmax=((i + 1) * p_gc, inf_eff, t_SiN)\n", + " )\n", + "\n", + " return td.Structure(geometry=gratings, medium=SiN)\n", + "\n", + "\n", + "waveguide = td.Structure(\n", + " geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(0, inf_eff, t_SiN)),\n", + " medium=SiN,\n", + ")\n", + "\n", + "oxide_layer = td.Structure(\n", + " geometry=td.Box.from_bounds(\n", + " rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, t_SiN + t_clad)\n", + " ),\n", + " medium=SiO2,\n", + ")\n", + "\n", + "\n", + "def make_2D_SiN_grating_sim(w_gc: float, p_gc: float, x_fiber: float) -> td.Simulation:\n", + " \"\"\"\n", + " Creates a simulation for a 2D SiN grating coupler.\n", + "\n", + " Parameters:\n", + " w_gc (float): Width of the etched region.\n", + " p_gc (float): Period of the grating.\n", + " x_fiber (float): x-position of the fiber center.\n", + "\n", + " Returns:\n", + " td.Simulation: The Tidy3D simulation object.\n", + " \"\"\"\n", + " gratings = make_2D_SiN_grating(w_gc, p_gc) # Create the grating structure\n", + "\n", + " # Create a Gaussian beam source representing the input fiber mode\n", + " gaussian_beam = td.GaussianBeam(\n", + " center=(x_fiber, 0, t_SiN + t_clad + lda0 / 4),\n", + " size=(2 * mfd, td.inf, 0),\n", + " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", + " pol_angle=np.pi / 2,\n", + " angle_theta=theta,\n", + " angle_phi=0,\n", + " direction=\"-\",\n", + " waist_radius=mfd / 2,\n", + " waist_distance=0,\n", + " )\n", + "\n", + " # Simulation domain box\n", + " sim_box = td.Box.from_bounds(\n", + " rmin=(-lda0, 0, -lda0), rmax=(n_gc * p_gc + lda0, 0, t_SiN + t_clad + lda0)\n", + " )\n", + "\n", + " # Create the simulation\n", + " sim = td.Simulation(\n", + " center=sim_box.center,\n", + " size=sim_box.size,\n", + " grid_spec=grid_spec,\n", + " run_time=run_time,\n", + " structures=[oxide_layer, gratings, waveguide],\n", + " sources=[gaussian_beam],\n", + " monitors=[mode_monitor],\n", + " boundary_spec=boundary_spec,\n", + " )\n", + " return sim" + ] + }, + { + "cell_type": "markdown", + "id": "6bd2a69f", + "metadata": {}, + "source": [ + "Create a single simulation to verify the setup and visualize the permittivity distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3a40871d-40ad-4f31-8374-cd8354c08aa5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim = make_2D_SiN_grating_sim(w_gc=0.5, p_gc=1.2, x_fiber=6)\n", + "sim.plot_eps(y=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8307f80b-317f-4bdc-8487-85e8c4022513", + "metadata": {}, + "outputs": [], + "source": [ + "def coupling_efficiency(sim_data: td.SimulationData) -> dict:\n", + " \"\"\"\n", + " Calculates the coupling efficiency from simulation data.\n", + "\n", + " Parameters\n", + " ----------\n", + " sim_data : td.SimulationData\n", + " The simulation data containing mode amplitudes.\n", + "\n", + " Returns\n", + " -------\n", + " dict\n", + " A dictionary containing the coupling efficiency in dB.\n", + " \"\"\"\n", + " # Extract the amplitude of the fundamental mode (mode_index=0) propagating in the backward direction (\"-\") at the central frequency (freq0)\n", + " amp = sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"-\", f=freq0).values\n", + " return {\"coupling efficiency\": 20 * np.log10(np.abs(amp))}" + ] + }, + { + "cell_type": "markdown", + "id": "86c3f2a4", + "metadata": {}, + "source": [ + "Now we are ready to perform the parameter sweep (grid search) using Tidy3D's `Design` plugin. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5e1c04a3-13bd-4d60-9079-a253d705aad9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
11:39:50 Eastern Standard Time Running 180 Simulations                          \n",
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\n" + ], + "text/plain": [ + "\u001b[2;36m11:39:50 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m180\u001b[0m Simulations \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define parameters and bounds\n", + "params = [\n", + " tdd.ParameterFloat(name=\"w_gc\", span=(0.4, 0.5), num_points=6),\n", + " tdd.ParameterFloat(name=\"p_gc\", span=(1.0, 1.2), num_points=6),\n", + " tdd.ParameterFloat(name=\"x_fiber\", span=(4.8, 5.2), num_points=5),\n", + "]\n", + "\n", + "# Design optimization method (grid search)\n", + "method = tdd.MethodGrid()\n", + "\n", + "# Create a design space and run the sweep\n", + "design_space = tdd.DesignSpace(method=method, parameters=params, path_dir=\"./data\")\n", + "results = design_space.run(make_2D_SiN_grating_sim, coupling_efficiency)" + ] + }, + { + "cell_type": "markdown", + "id": "9ffbb16e", + "metadata": {}, + "source": [ + "After the sweep is done, we can pick the optimal design parameters. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bb1d3849-90ef-47ff-b192-5f2d8376e6a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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w_gcp_gcx_fibercoupling efficiency
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\n", + "
" + ], + "text/plain": [ + " w_gc p_gc x_fiber coupling efficiency\n", + "72 0.44 1.08 5.0 -5.882929\n", + "73 0.44 1.08 5.1 -5.883616\n", + "71 0.44 1.08 4.9 -5.884305\n", + "74 0.44 1.08 5.2 -5.886312\n", + "70 0.44 1.08 4.8 -5.887364\n", + ".. ... ... ... ...\n", + "150 0.40 1.20 4.8 -24.129137\n", + "151 0.40 1.20 4.9 -24.406663\n", + "152 0.40 1.20 5.0 -24.685795\n", + "153 0.40 1.20 5.1 -24.970030\n", + "154 0.40 1.20 5.2 -25.254913\n", + "\n", + "[180 rows x 4 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = results.to_dataframe() # Convert the results to a pandas DataFrame\n", + "\n", + "# Pick the best design\n", + "best_row = df.loc[df[\"coupling efficiency\"].idxmax()]\n", + "best_w_gc, best_p_gc, best_x_fiber = best_row[[\"w_gc\", \"p_gc\", \"x_fiber\"]]\n", + "df.sort_values(by=\"coupling efficiency\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "fff4dba1", + "metadata": {}, + "source": [ + "## Periodic Si Grating Reflector Design\n", + "To improve the coupling efficiency of the grating coupler, we add a bottom grating in the silicon layer to reflect leakage light upwards. We need to optimize the grating period and duty cycle to achieve maximum reflection." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "57f06032-4e88-4b0b-a83b-6e595dfbc073", + "metadata": {}, + "outputs": [], + "source": [ + "def make_2D_Si_grating(w_gr: float, p_gr: float, z_gr: float) -> \"td.Structure\":\n", + " \"\"\"\n", + " Creates a 2D silicon grating structure.\n", + "\n", + " Args:\n", + " w_gr: Width of the etched region.\n", + " p_gr: Period of the grating.\n", + " z_gr: The z-coordinate of the bottom surface of the grating.\n", + "\n", + " Returns:\n", + " A Tidy3D Structure object representing the entire grating.\n", + " \"\"\"\n", + " offset = -2 # Initial offset for the grating placement\n", + "\n", + " # Loop to create multiple grating teeth\n", + " gratings = 0\n", + " for i in range(50):\n", + " gratings += td.Box.from_bounds(\n", + " rmin=(offset + w_gr + i * p_gr, -inf_eff, -z_gr),\n", + " rmax=(offset + (i + 1) * p_gr, inf_eff, -z_gr + t_Si),\n", + " )\n", + "\n", + " return td.Structure(geometry=gratings, medium=Si)\n", + "\n", + "\n", + "# Create a Gaussian beam at the optimal position\n", + "gaussian_beam = td.GaussianBeam(\n", + " center=(best_x_fiber, 0, t_SiN + t_clad + lda0 / 4),\n", + " size=(2 * mfd, td.inf, 0),\n", + " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", + " pol_angle=np.pi / 2,\n", + " angle_theta=theta,\n", + " angle_phi=0,\n", + " direction=\"-\",\n", + " waist_radius=mfd / 2,\n", + " waist_distance=0,\n", + ")\n", + "\n", + "\n", + "def make_2D_Si_grating_sim(w_gr: float, p_gr: float, z_gr: float = 1) -> \"td.Simulation\":\n", + " \"\"\"\n", + " Creates a Tidy3D simulation for a 2D silicon grating.\n", + "\n", + " Args:\n", + " w_gr: Width of the etched region.\n", + " p_gr: Period of the grating.\n", + " z_gr: The z-coordinate of the bottom surface of the grating.\n", + "\n", + " Returns:\n", + " A Tidy3D Simulation object.\n", + " \"\"\"\n", + " # Create the grating structure\n", + " gratings = make_2D_Si_grating(w_gr, p_gr, z_gr)\n", + "\n", + " # Define the substrate structure\n", + " substrate = td.Structure(\n", + " geometry=td.Box.from_bounds(\n", + " rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -z_gr - t_box)\n", + " ),\n", + " medium=Si,\n", + " )\n", + "\n", + " # Define a flux monitor to measure reflection\n", + " flux_monitor = td.FluxMonitor(\n", + " center=(0, 0, t_SiN + t_clad + lda0 / 2),\n", + " size=(td.inf, td.inf, 0),\n", + " freqs=[freq0],\n", + " normal_dir=\"+\",\n", + " name=\"flux\",\n", + " )\n", + "\n", + " # Simulation domain box\n", + " sim_box = td.Box.from_bounds(\n", + " rmin=(-lda0, 0, -z_gr - t_box - lda0),\n", + " rmax=(20 * p_gr + lda0, 0, t_SiN + t_clad + lda0),\n", + " )\n", + "\n", + " # Create the simulation\n", + " sim = td.Simulation(\n", + " center=sim_box.center,\n", + " size=sim_box.size,\n", + " grid_spec=grid_spec,\n", + " run_time=run_time,\n", + " structures=[oxide_layer, gratings, substrate],\n", + " sources=[gaussian_beam],\n", + " monitors=[flux_monitor],\n", + " boundary_spec=boundary_spec,\n", + " )\n", + " return sim" + ] + }, + { + "cell_type": "markdown", + "id": "66dc7ede", + "metadata": {}, + "source": [ + "Visualize the simulation setup." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "70e50568-19b9-4143-ba1d-84d69ef43510", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim = make_2D_Si_grating_sim(w_gr=0.2, p_gr=0.5)\n", + "sim.plot(y=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c2a39651-1e60-4949-9577-f24cf9276c03", + "metadata": {}, + "outputs": [], + "source": [ + "def reflectivity(sim_data: td.SimulationData) -> dict:\n", + " \"\"\"\n", + " Extracts the reflectivity from the simulation data.\n", + "\n", + " Parameters:\n", + " sim_data (td.SimulationData): The simulation data containing flux results.\n", + "\n", + " Returns:\n", + " dict: A dictionary containing the reflectivity values.\n", + " \"\"\"\n", + " # Extract the flux values from the simulation data\n", + " r = sim_data[\"flux\"].flux.values\n", + " return {\"reflectivity\": r}" + ] + }, + { + "cell_type": "markdown", + "id": "5693f9d1", + "metadata": {}, + "source": [ + "Similar to the previous section, we will perform a parameter sweep to optimize the Si grating period and duty cycle." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e6433c1a-2078-4b2e-acd8-ac5a03dce7a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
11:44:43 Eastern Standard Time Running 121 Simulations                          \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:44:43 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m121\u001b[0m Simulations \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Design parameters and bounds\n", + "params = [\n", + " tdd.ParameterFloat(name=\"w_gr\", span=(0.3, 0.5), num_points=11),\n", + " tdd.ParameterFloat(name=\"p_gr\", span=(0.7, 1.0), num_points=11),\n", + "]\n", + "\n", + "# Perform parameter sweep\n", + "design_space = tdd.DesignSpace(method=method, parameters=params, path_dir=\"./data\")\n", + "results = design_space.run(make_2D_Si_grating_sim, reflectivity)" + ] + }, + { + "cell_type": "markdown", + "id": "16894b2a", + "metadata": {}, + "source": [ + "From the results, we find the parameters that yield the highest reflection." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "66c8c022-422c-478a-b5de-e1d282c69dc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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121 rows × 3 columns

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" + ], + "text/plain": [ + " w_gr p_gr reflectivity\n", + "73 0.44 0.88 [0.9637354]\n", + "74 0.46 0.88 [0.95940167]\n", + "59 0.38 0.85 [0.95864624]\n", + "75 0.48 0.88 [0.94225246]\n", + "87 0.50 0.91 [0.93560386]\n", + ".. ... ... ...\n", + "113 0.36 1.00 [0.20038801]\n", + "99 0.30 0.97 [0.19345635]\n", + "112 0.34 1.00 [0.18722352]\n", + "111 0.32 1.00 [0.17701866]\n", + "110 0.30 1.00 [0.16915095]\n", + "\n", + "[121 rows x 3 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = results.to_dataframe()\n", + "\n", + "best_row = df.loc[df[\"reflectivity\"].idxmax()]\n", + "best_w_gr, best_p_gr = best_row[[\"w_gr\", \"p_gr\"]]\n", + "\n", + "df.sort_values(by=\"reflectivity\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "a65cdc6a", + "metadata": {}, + "source": [ + "## Interlayer Distance Optimization\n", + "\n", + "Here we combine the optimized SiN grating and the optimized Si reflector into a single simulation and optimize the vertical separation `z_gr` to ensure constructive interference and therefore maximum coupling efficiency." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f489ef74-4ecc-43d4-8428-9927e9ff72d5", + "metadata": {}, + "outputs": [], + "source": [ + "def make_2D_full_grating_sim(z_gr: float) -> td.Simulation:\n", + " \"\"\"\n", + " Creates a simulation of the full coupler structure including SiN and Si gratings.\n", + "\n", + " Parameters\n", + " ----------\n", + " z_gr : float\n", + " The z-position for the Si grating structure.\n", + "\n", + " Returns\n", + " -------\n", + " td.Simulation\n", + " Tidy3D simulation object.\n", + " \"\"\"\n", + " # Create the SiN grating structure using optimal width and period\n", + " gratings_SiN = make_2D_SiN_grating(best_w_gc, best_p_gc)\n", + "\n", + " # Create the Si grating structure using optimal width, period, and the given z-position\n", + " gratings_Si = make_2D_Si_grating(best_w_gr, best_p_gr, z_gr)\n", + "\n", + " # Define the silicon substrate structure\n", + " substrate = td.Structure(\n", + " geometry=td.Box.from_bounds(\n", + " rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -z_gr - t_box)\n", + " ),\n", + " medium=Si,\n", + " )\n", + "\n", + " # Calculate the SiN grating length\n", + " l = best_p_gc * n_gc\n", + "\n", + " # Simulation domain box\n", + " sim_box = td.Box.from_bounds(\n", + " rmin=(-lda0, 0, -z_gr - t_box - lda0), rmax=(l + lda0, 0, t_SiN + t_clad + lda0)\n", + " )\n", + "\n", + " # Construct the simulation object\n", + " sim = td.Simulation(\n", + " center=sim_box.center,\n", + " size=sim_box.size,\n", + " grid_spec=grid_spec,\n", + " run_time=run_time,\n", + " structures=[oxide_layer, gratings_SiN, gratings_Si, waveguide, substrate],\n", + " sources=[gaussian_beam],\n", + " monitors=[mode_monitor],\n", + " boundary_spec=boundary_spec,\n", + " )\n", + " return sim" + ] + }, + { + "cell_type": "markdown", + "id": "29f3387f", + "metadata": {}, + "source": [ + "Visualize and verify the simulation setup." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "25681264-d211-4e11-869e-2aaa8199b3bf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim = make_2D_full_grating_sim(2)\n", + "\n", + "sim.plot_eps(y=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "42ebff02", + "metadata": {}, + "source": [ + "Now we perform parameter sweep of the interlayer distance to maximize the coupling efficiency. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "399eb8b7-19c8-4e67-b1cf-6383e20b7c93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
11:48:01 Eastern Standard Time Running 21 Simulations                           \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:48:01 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m21\u001b[0m Simulations \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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z_grcoupling efficiency
21.10-1.404425
131.65-1.484064
31.15-1.526486
141.70-1.717543
121.60-1.774490
11.05-1.820092
41.20-1.914278
151.75-2.289349
51.25-2.713833
111.55-2.773200
01.00-3.106520
161.80-3.638100
101.50-4.675568
61.30-5.345168
202.00-7.289271
171.85-7.731516
91.45-8.319647
71.35-12.881006
191.95-13.697595
181.90-17.376482
81.40-19.452692
\n", + "
" + ], + "text/plain": [ + " z_gr coupling efficiency\n", + "2 1.10 -1.404425\n", + "13 1.65 -1.484064\n", + "3 1.15 -1.526486\n", + "14 1.70 -1.717543\n", + "12 1.60 -1.774490\n", + "1 1.05 -1.820092\n", + "4 1.20 -1.914278\n", + "15 1.75 -2.289349\n", + "5 1.25 -2.713833\n", + "11 1.55 -2.773200\n", + "0 1.00 -3.106520\n", + "16 1.80 -3.638100\n", + "10 1.50 -4.675568\n", + "6 1.30 -5.345168\n", + "20 2.00 -7.289271\n", + "17 1.85 -7.731516\n", + "9 1.45 -8.319647\n", + "7 1.35 -12.881006\n", + "19 1.95 -13.697595\n", + "18 1.90 -17.376482\n", + "8 1.40 -19.452692" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Design parameter and bound\n", + "params = [\n", + " tdd.ParameterFloat(name=\"z_gr\", span=(1, 2), num_points=21),\n", + "]\n", + "\n", + "# Perform parameter sweep\n", + "design_space = tdd.DesignSpace(method=method, parameters=params, path_dir=\"./data\")\n", + "results = design_space.run(make_2D_full_grating_sim, coupling_efficiency)\n", + "\n", + "# Plot the coupling efficiency as a function of z_gr\n", + "df = results.to_dataframe()\n", + "plt.plot(df[\"z_gr\"], df[\"coupling efficiency\"], marker=\"o\")\n", + "plt.xlabel(\"z_gr\")\n", + "plt.ylabel(\"Coupling efficiency (dB)\")\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Extract the best z_gr\n", + "best_row = df.loc[df[\"coupling efficiency\"].idxmax()]\n", + "best_z_gr = best_row[\"z_gr\"]\n", + "df.sort_values(by=\"coupling efficiency\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "id": "bb5fa5cc", + "metadata": {}, + "source": [ + "Plot the coupling efficiency spectrum for the current best design. A peak coupling efficiency of ~1.5dB is achieved." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eaa5e129-a533-4d79-9902-0ba9e52a5ce7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
11:48:40 Eastern Standard Time Created task 'Optimized 2D uniform grating' with \n",
+       "                               task_id                                          \n",
+       "                               'fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c' and  \n",
+       "                               task_type 'FDTD'.                                \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:48:40 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'Optimized 2D uniform grating'\u001b[0m with \n", + "\u001b[2;36m \u001b[0mtask_id \n", + "\u001b[2;36m \u001b[0m\u001b[32m'fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c'\u001b[0m and \n", + "\u001b[2;36m \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c'.     \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", + "\u001b[2;36m \u001b[0m\u001b]8;id=560747;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=721670;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[2;36m \u001b[0m\u001b]8;id=560747;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=426292;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=560747;https://tidy3d.simulation.cloud/workbench?taskId=fdve-b4d02f11-92cc-4220-aebb-1065f12e8c3c\u001b\\\u001b[32m-b4d02f11-92cc-4220-aebb-1065f12e8c3c'\u001b[0m\u001b]8;;\u001b\\. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
                               Task folder: 'default'.                          \n",
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\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=746038;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7727fe36273d45baaeb04ead82cea8f2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+      ],
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+       "
11:48:41 Eastern Standard Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:48:41 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost \n", + "\u001b[2;36m \u001b[0mdepends on task execution details. Use \n", + "\u001b[2;36m \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n", + "\u001b[2;36m \u001b[0mFlexCredit cost after a simulation run. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
11:48:42 Eastern Standard Time status = success                                 \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:48:42 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "342f8120015a44c6b2d4825afeeca078", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
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+     },
+     "metadata": {},
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+    {
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+       "
11:48:43 Eastern Standard Time loading simulation from simulation_data.hdf5     \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m11:48:43 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim = make_2D_full_grating_sim(best_z_gr)\n", + "sim_data = web.run(sim, \"Optimized 2D uniform grating\")\n", + "ce = 20 * np.log10(np.abs(sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"-\").values))\n", + "plt.plot(ldas, ce, linewidth=3)\n", + "plt.ylim(-5, 0)\n", + "plt.grid()\n", + "plt.xlabel(\"Wavelength (µm)\")\n", + "plt.ylabel(\"Coupling efficiency (dB)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bc90696b", + "metadata": {}, + "source": [ + "## Inverse Design Optimization of the SiN Grating\n", + "\n", + "Finally, we perform gradient-based optimization (inverse design) to fine tune the grating. This allows each tooth width and spacing to vary independently, matching the mode profile of the fiber for maximum coupling efficiency." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e12206c7-f737-4242-9f72-690189a43ff0", + "metadata": {}, + "outputs": [], + "source": [ + "def make_2D_apodized_grating_sim(design_params: np.ndarray) -> td.Simulation:\n", + " \"\"\"\n", + " Creates a 2D Tidy3D simulation for an aperiodic grating coupler.\n", + "\n", + " Parameters\n", + " ----------\n", + " design_params : list[float]\n", + " A list of widths defining the grating structure.\n", + " Even indices (0, 2, ...) correspond to etched widths.\n", + " Odd indices (1, 3, ...) correspond to SiN tooth widths.\n", + "\n", + " Returns\n", + " -------\n", + " td.Simulation\n", + " Tidy3D simulation object.\n", + " \"\"\"\n", + "\n", + " widths_SiN = design_params[1::2] # SiN tooth widths\n", + " widths_void = design_params[::2] # Etched widths\n", + "\n", + " # Create SiN grating geometries from the given widths\n", + " gratings = 0\n", + " center = 0\n", + " for width_SiN, width_void in zip(widths_SiN, widths_void):\n", + " center += width_void + width_SiN / 2 # Update center position\n", + " size = width_SiN\n", + " gratings += td.Box(center=(center, 0, t_SiN / 2), size=(size, td.inf, t_SiN))\n", + " center += width_SiN / 2 # Update center position\n", + "\n", + " gratings_SiN = td.Structure(geometry=gratings, medium=SiN)\n", + "\n", + " # Create Si grating reflector structure\n", + " gratings_Si = make_2D_Si_grating(best_w_gr, best_p_gr, z_gr=best_z_gr)\n", + "\n", + " # Create Si substrate structure\n", + " substrate = td.Structure(\n", + " geometry=td.Box.from_bounds(\n", + " rmin=(-inf_eff, -inf_eff, -inf_eff),\n", + " rmax=(inf_eff, inf_eff, -best_z_gr - t_box),\n", + " ),\n", + " medium=Si,\n", + " )\n", + "\n", + " l = 16 # Fix simulation domain size in the x direction\n", + "\n", + " # Define simulation domain box\n", + " sim_box = td.Box.from_bounds(\n", + " rmin=(-lda0, 0, -best_z_gr - t_box - lda0),\n", + " rmax=(l + lda0, 0, t_SiN + t_clad + lda0),\n", + " )\n", + "\n", + " # Create a Tidy3D simulation\n", + " sim = td.Simulation(\n", + " center=sim_box.center,\n", + " size=sim_box.size,\n", + " grid_spec=grid_spec,\n", + " run_time=run_time,\n", + " structures=[oxide_layer, gratings_SiN, gratings_Si, waveguide, substrate],\n", + " sources=[gaussian_beam],\n", + " monitors=[mode_monitor],\n", + " boundary_spec=boundary_spec,\n", + " )\n", + " return sim" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5a5a8072-663c-4d2d-940a-46ed754f5c39", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "design_params0 = np.array([best_w_gc, best_p_gc - best_w_gc] * n_gc)\n", + "sim = make_2D_apodized_grating_sim(design_params0)\n", + "\n", + "sim.plot_eps(y=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ce72c26", + "metadata": {}, + "source": [ + "Define the objective function, which is the coupling efficiency at 1550 nm." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2c1305db-37f1-4e4a-9ad6-76425d0376d6", + "metadata": {}, + "outputs": [], + "source": [ + "def J(design_params: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Compute the coupling efficiency (CE) for a given set of design parameters.\n", + "\n", + " Parameters\n", + " ----------\n", + " design_params: np.ndarray\n", + " A 1D numpy array containing the design parameters for the grating.\n", + "\n", + " Returns\n", + " -------\n", + " The coupling efficiency\n", + " \"\"\"\n", + "\n", + " sim = make_2D_apodized_grating_sim(design_params)\n", + " sim_data = web.run(sim, task_name=\"GC_invdes\", verbose=False)\n", + "\n", + " # Extract the complex amplitude of the desired mode at frequency freq0.\n", + " amp = sim_data[\"mode\"].amps.sel(mode_index=0, f=freq0, direction=\"-\").values\n", + "\n", + " # Coupling efficiency is the squared magnitude of the amplitude.\n", + " ce = np.abs(amp) ** 2\n", + "\n", + " return ce" + ] + }, + { + "cell_type": "markdown", + "id": "7e6cf676-980a-49de-bf22-6eebdd46ed26", + "metadata": {}, + "source": [ + "Before actually running the inverse design loop, we first test the gradient calculation to ensure it works well. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "509507a1-054f-400e-905a-f6b67765c0ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7236100065129941\n", + "[ 0.22307575 0.56603593 0.51706766 0.41977405 0.3905873 -0.03293696\n", + " -0.27843539 -0.47574742 -0.70943264 -0.75728147 -0.41855601 -0.49909601\n", + " -0.2300799 -0.29551694 -0.27728662 -0.32593653 -0.24543892 -0.26322852\n", + " -0.14479698 -0.15561174 -0.11804074 -0.13953611 -0.09500033 -0.10664506\n", + " -0.05071084 -0.06030557 -0.01361688 -0.01446665]\n" + ] + } + ], + "source": [ + "dJ = ag.value_and_grad(J)\n", + "val, grad = dJ(design_params0)\n", + "print(val)\n", + "print(grad)" + ] + }, + { + "cell_type": "markdown", + "id": "de20b62b-8167-4736-8816-5a0ea3383ceb", + "metadata": {}, + "source": [ + "Now we iteratively improve the design. Here we use the Adam optimizer from `optax`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7564381c-1aed-4eb4-961f-6dfc9e7bdd02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step = 1\n", + "\tJ = 7.236e-01\n", + "step = 2\n", + "\tJ = 7.380e-01\n", + "step = 3\n", + "\tJ = 7.433e-01\n", + "step = 4\n", + "\tJ = 7.430e-01\n", + "step = 5\n", + "\tJ = 7.416e-01\n", + "step = 6\n", + "\tJ = 7.438e-01\n", + "step = 7\n", + "\tJ = 7.455e-01\n", + "step = 8\n", + "\tJ = 7.467e-01\n", + "step = 9\n", + "\tJ = 7.468e-01\n", + "step = 10\n", + "\tJ = 7.481e-01\n", + "step = 11\n", + "\tJ = 7.501e-01\n", + "step = 12\n", + "\tJ = 7.520e-01\n", + "step = 13\n", + "\tJ = 7.540e-01\n", + "step = 14\n", + "\tJ = 7.548e-01\n", + "step = 15\n", + "\tJ = 7.564e-01\n", + "step = 16\n", + "\tJ = 7.559e-01\n", + "step = 17\n", + "\tJ = 7.570e-01\n", + "step = 18\n", + "\tJ = 7.581e-01\n", + "step = 19\n", + "\tJ = 7.588e-01\n", + "step = 20\n", + "\tJ = 7.604e-01\n", + "step = 21\n", + "\tJ = 7.615e-01\n", + "step = 22\n", + "\tJ = 7.621e-01\n", + "step = 23\n", + "\tJ = 7.627e-01\n", + "step = 24\n", + "\tJ = 7.632e-01\n", + "step = 25\n", + "\tJ = 7.636e-01\n", + "step = 26\n", + "\tJ = 7.646e-01\n", + "step = 27\n", + "\tJ = 7.635e-01\n", + "step = 28\n", + "\tJ = 7.646e-01\n", + "step = 29\n", + "\tJ = 7.655e-01\n", + "step = 30\n", + "\tJ = 7.665e-01\n", + "step = 31\n", + "\tJ = 7.676e-01\n", + "step = 32\n", + "\tJ = 7.686e-01\n", + "step = 33\n", + "\tJ = 7.696e-01\n", + "step = 34\n", + "\tJ = 7.698e-01\n", + "step = 35\n", + "\tJ = 7.703e-01\n", + "step = 36\n", + "\tJ = 7.705e-01\n", + "step = 37\n", + "\tJ = 7.720e-01\n", + "step = 38\n", + "\tJ = 7.721e-01\n", + "step = 39\n", + "\tJ = 7.737e-01\n", + "step = 40\n", + "\tJ = 7.759e-01\n", + "step = 41\n", + "\tJ = 7.771e-01\n", + "step = 42\n", + "\tJ = 7.759e-01\n", + "step = 43\n", + "\tJ = 7.761e-01\n", + "step = 44\n", + "\tJ = 7.781e-01\n", + "step = 45\n", + "\tJ = 7.801e-01\n", + "step = 46\n", + "\tJ = 7.814e-01\n", + "step = 47\n", + "\tJ = 7.827e-01\n", + "step = 48\n", + "\tJ = 7.822e-01\n", + "step = 49\n", + "\tJ = 7.819e-01\n", + "step = 50\n", + "\tJ = 7.844e-01\n", + "step = 51\n", + "\tJ = 7.863e-01\n", + "step = 52\n", + "\tJ = 7.872e-01\n", + "step = 53\n", + "\tJ = 7.871e-01\n", + "step = 54\n", + "\tJ = 7.884e-01\n", + "step = 55\n", + "\tJ = 7.907e-01\n", + "step = 56\n", + "\tJ = 7.923e-01\n", + "step = 57\n", + "\tJ = 7.927e-01\n", + "step = 58\n", + "\tJ = 7.936e-01\n", + "step = 59\n", + "\tJ = 7.961e-01\n", + "step = 60\n", + "\tJ = 7.974e-01\n", + "step = 61\n", + "\tJ = 7.979e-01\n", + "step = 62\n", + "\tJ = 7.986e-01\n", + "step = 63\n", + "\tJ = 8.003e-01\n", + "step = 64\n", + "\tJ = 8.017e-01\n", + "step = 65\n", + "\tJ = 8.017e-01\n", + "step = 66\n", + "\tJ = 8.037e-01\n", + "step = 67\n", + "\tJ = 8.047e-01\n", + "step = 68\n", + "\tJ = 8.062e-01\n", + "step = 69\n", + "\tJ = 8.071e-01\n", + "step = 70\n", + "\tJ = 8.078e-01\n", + "step = 71\n", + "\tJ = 8.078e-01\n", + "step = 72\n", + "\tJ = 8.100e-01\n", + "step = 73\n", + "\tJ = 8.103e-01\n", + "step = 74\n", + "\tJ = 8.109e-01\n", + "step = 75\n", + "\tJ = 8.114e-01\n", + "step = 76\n", + "\tJ = 8.110e-01\n", + "step = 77\n", + "\tJ = 8.124e-01\n", + "step = 78\n", + "\tJ = 8.138e-01\n", + "step = 79\n", + "\tJ = 8.142e-01\n", + "step = 80\n", + "\tJ = 8.134e-01\n" + ] + } + ], + "source": [ + "# Hyperparameters\n", + "num_steps = 80\n", + "learning_rate = 0.005\n", + "min_feature = 0.1\n", + "# Initialize the adam optimizer with starting parameters\n", + "params = design_params0\n", + "optimizer = optax.adam(learning_rate=learning_rate)\n", + "opt_state = optimizer.init(params)\n", + "\n", + "# Store history\n", + "J_history = []\n", + "params_history = []\n", + "\n", + "for i in range(num_steps):\n", + " # Compute gradient and current objective function value\n", + " value, gradient = dJ(params)\n", + "\n", + " # Outputs\n", + " print(f\"step = {i + 1}\")\n", + " print(f\"\\tJ = {value:.3e}\")\n", + "\n", + " # Compute and apply updates to the optimizer based on gradient\n", + " updates, opt_state = optimizer.update(-gradient, opt_state, params)\n", + " params[:] = optax.apply_updates(params, updates)\n", + " params = np.clip(params, a_min=min_feature, a_max=None)\n", + "\n", + " # Save history\n", + " J_history.append(value)\n", + " params_history.append(params)" + ] + }, + { + "cell_type": "markdown", + "id": "9c992e81-7e32-4d91-b0d0-58a46e9e20b8", + "metadata": {}, + "source": [ + "After the optimization, we can plot the objective function history to visualize the progress. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0ec77f06-4ec4-4e7a-a2e4-de5af96e9094", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(J_history, c=\"black\")\n", + "plt.xlabel(\"Iterations\")\n", + "plt.ylabel(\"Objective function (coupling efficiency)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "86b468ff-aec7-437c-ae02-f7fef8277ed5", + "metadata": {}, + "source": [ + "Plot the final design." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d3882bd2-cd79-4363-8288-93e03af0a5e4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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12:43:24 Eastern Standard Time Created task 'Final design' with task_id         \n",
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                               View task using web UI at                        \n",
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12:43:25 Eastern Standard Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
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12:43:26 Eastern Standard Time status = success                                 \n",
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12:43:27 Eastern Standard Time loading simulation from simulation_data.hdf5     \n",
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\n" + ], + "text/plain": [ + "\u001b[2;36m12:43:27 Eastern Standard Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim_data_final = web.run(sim_final, \"Final design\")\n", + "\n", + "ce_final = 20 * np.log10(\n", + " np.abs(sim_data_final[\"mode\"].amps.sel(mode_index=0, direction=\"-\").values)\n", + ")\n", + "plt.plot(ldas, ce_final, linewidth=3, label=\"Adjoint optimized\")\n", + "plt.plot(ldas, ce, linewidth=3, label=\"Periodic\")\n", + "plt.ylim(-5, 0)\n", + "plt.grid()\n", + "plt.xlabel(\"Wavelength (µm)\")\n", + "plt.ylabel(\"Coupling efficiency (dB)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7e55ff21-d538-439f-8475-46e0e7b3032e", + "metadata": {}, + "source": [ + "Finally, we can visually inspect the change of the design parameters. Interestingly, we can see that the most significant change occurs in the first few grating periods. The middle parts of the grating is largely unchanged." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "724ba110-d790-44b9-bf9f-26d4e666b7b0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.plot(opt_params, \"o-\", label=\"Adjoint optimized\", alpha=0.8)\n", + "plt.plot(design_params0, \"o-\", label=\"Periodic\", alpha=0.8)\n", + "plt.xlabel(\"Parameter index\")\n", + "plt.ylabel(\"Value\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2c245091-101d-406f-8949-264b8c5f2f77", + "metadata": {}, + "source": [ + "## Final Notes\n", + "\n", + "In this notebook we perform all simulations in 2D, which is a cost effective and reasonably accurate way of modeling and optimizing the grating coupler. In the next step, we need to create the full grating in 3D (either a linear or focusing configuration) to verify and possibly further optimize the performance. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cad42aad-79e7-4856-8292-c80e973b15cb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "applications": [ + "Passive photonic integrated circuit components" + ], + "description": "This notebook demonstrates how to design a bilayer grating coupler and use inverse design optimization to optimize it in Tidy3D FDTD.", + "feature_image": "./img/bilayer_GC.png", + "features": [ + "Adjoint inverse design", + "Parameter sweep", + "2D simulation", + "Design plugin" + ], + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "keywords": "silicon photonics, silicon nitride, PIC, integrated photonics, grating 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": "3.11.0" + }, + "title": "Design optimization of a bilayer SiN/Si grating coupler in Tidy3D | Flexcompute" + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/case_studies/pic.rst b/docs/case_studies/pic.rst index 250ec8d1..a6ca2486 100644 --- a/docs/case_studies/pic.rst +++ b/docs/case_studies/pic.rst @@ -16,6 +16,7 @@ Passive photonic integrated circuit (PIC) components form the backbone of many o ../../EffectiveIndexApproximation ../../GratingCoupler ../../FocusedApodGC + ../../BilayerSiNSiGC ../../VerticalGratingCoupler ../../MMI1x4 ../../MMIPowerSplitter2x2 diff --git a/img/bilayer_GC.png b/img/bilayer_GC.png new file mode 100644 index 0000000000000000000000000000000000000000..9087dec2b173e6c87bea7f7c30376e77bb668ac7 GIT binary patch literal 167766 zcmeFZ^;?wf_XRqLSRfz@D5>Bejf4yhk}@zL-5@PBv~(!aB`qxt0wUd^A{`Ra-Ho)w zz_}lM&*%FOoF5O@#pMXhoqMmn_S*ZIz?X7Tw{MZ%g27<7rC*3E!eH3eFxZu!H?D!7 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