{
"cells": [
{
"cell_type": "markdown",
"id": "e06d909a",
"metadata": {},
"source": [
"# Plasmonic Yagi-Uda nanoantenna"
]
},
{
"cell_type": "markdown",
"id": "7d61adb8",
"metadata": {},
"source": [
"Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n",
"\n",
"Antennas are the fundamental building blocks for high-speed communication networks. The concept of an antenna is well-established, particularly in RF and microwave engineering, dating back over one century ago. An antenna transforms propagating electromagnetic waves to localized electromagnetic field and vice versa, depending on whether it is in the transmitting mode or receiving mode. Thus, it enables wireless communication and information transmission over long distances. \n",
"\n",
"Recent rapid developments in nanotechnology have sparked vast interest in constructing the optical counterpart of antennas by utilizing the plasmonic nature of metal at optical frequencies. The size of these antennas is usually in the order of 100 nm. Therefore, they are often termed plasmonic nanoantennas. As the demand for higher bandwidth information transmission keeps growing, plasmonic nanoantennas potentially be the technological cornerstone for future communication systems.\n",
"\n",
"In this example notebook, we demonstrate the modeling of a plasmonic Yagi-Uda nanoantenna made of aluminum nanorods excited by a point dipole source. The far-field radiation pattern is calculated. We show that the simulated plasmonic Yagi-Uda nanoantenna can achieve a high directivity, which is desirable in many applications. The model is based on [Tim H. Taminiau, Fernando D. Stefani, and Niek F. van Hulst, \"Enhanced directional excitation and emission of single emitters by a nano-optical Yagi-Uda antenna,\" Opt. Express 16, 10858-10866 (2008)](https://opg.optica.org/oe/fulltext.cfm?uri=oe-16-14-10858&id=167282).\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "8c4e33ec",
"metadata": {},
"source": [
"## Simulation Setup "
]
},
{
"cell_type": "markdown",
"id": "39e69ef7",
"metadata": {},
"source": [
"In this model, we are going to fit the refractive index of aluminum using data from the literature. Thus, we import the [DispersionFitter](../_autosummary/tidy3d.plugins.DispersionFitter.html) from the Tidy3D plugins."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1cd3ac66",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-27T21:13:37.464583Z",
"iopub.status.busy": "2023-03-27T21:13:37.464385Z",
"iopub.status.idle": "2023-03-27T21:13:38.779281Z",
"shell.execute_reply": "2023-03-27T21:13:38.778655Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tidy3d as td\n",
"import tidy3d.web as web\n",
"from tidy3d.plugins.dispersion import DispersionFitter\n"
]
},
{
"cell_type": "markdown",
"id": "d1868111",
"metadata": {},
"source": [
"As schematically shown above, a typical Yagi-Uda antenna consists of three components: a feed element that is excited by a source, a reflector element that suppresses the radiation in the backward direction, and an array of director elements that enhances the radiation in the forward direction. Usually, having a large number of director elements is beneficial for achieving a high directivity. In practice, we need to consider the footprint, fabrication constraints, costs, and so on. In this particular example, our Yagi-Uda antenna has three director elements. All elements are made of aluminum nanorods with rounded ends. \n",
"\n",
"The lengths and spacings of the elements are designed to achieve optimal performance at 570 nm wavelength. An initial design can be obtained by following the classical design principle of RF/microwave Yagi-Uda antennas. Since metals behave very differently in lower frequencies compared to optical frequencies, the parameters need to be optimized to account for the finite skin depth and ohmic loss. In this notebook, we skip the optimization process and only present the optimized design from the referenced paper. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f76b62d9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-27T21:13:38.781571Z",
"iopub.status.busy": "2023-03-27T21:13:38.781309Z",
"iopub.status.idle": "2023-03-27T21:13:38.799699Z",
"shell.execute_reply": "2023-03-27T21:13:38.799093Z"
}
},
"outputs": [],
"source": [
"lda0 = 0.57 # operation wavelength\n",
"freq0 = td.C_0 / lda0 # operation frequency\n"
]
},
{
"cell_type": "markdown",
"id": "7bfd9947",
"metadata": {},
"source": [
"The nanorods are made of aluminum. Before constructing the model, we first need to use the [DispersionFitter](../_autosummary/tidy3d.plugins.DispersionFitter.html) to fit the refractive index data of aluminum, which can be found in the [refractive index database](https://refractiveindex.info/). In particular, we use the data from [McPeak et al. 2015](https://pubs.acs.org/doi/10.1021/ph5004237). Since we are only interested in the antenna response at 570 nm, we only need to fit the refractive index in the vicinity of the operation wavelength.\n",
"\n",
"The fitting results in a RMS error about of 0.01, which is reasonably good."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0c4d1567",
"metadata": {
"execution": {
"iopub.execute_input": "2023-03-27T21:13:38.801844Z",
"iopub.status.busy": "2023-03-27T21:13:38.801702Z",
"iopub.status.idle": "2023-03-27T21:13:52.849376Z",
"shell.execute_reply": "2023-03-27T21:13:52.849011Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "782b365d38dc4bf2991673ed8ffcc13b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
\n", "\n" ], "text/plain": [ "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fname = \"misc/McPeak.csv\" # read the refractive index data from a csv file\n", "fitter = DispersionFitter.from_file(fname, delimiter=\",\") # construct a fitter\n", "al, rms_error = fitter.fit(num_poles=6, tolerance_rms=2e-2, num_tries=50)\n" ] }, { "cell_type": "markdown", "id": "c1f03abd", "metadata": {}, "source": [ "Next, we construct the Yagi-Uda antenna by individually constructing the feed, reflector, and three directors. Each element consists of a cylinder and two spheres that represent the rounded caps on each end. For convenience, we define a function to build the antenna structures since it will be used repeatedly in the next section of this notebook." ] }, { "cell_type": "code", "execution_count": 4, "id": "db39d6c3", "metadata": { "execution": { "iopub.execute_input": "2023-03-27T21:13:53.062058Z", "iopub.status.busy": "2023-03-27T21:13:53.061922Z", "iopub.status.idle": "2023-03-27T21:13:53.087474Z", "shell.execute_reply": "2023-03-27T21:13:53.086966Z" } }, "outputs": [], "source": [ "# L_f is the length of the feed element\n", "# r is the radius of the nanorods.\n", "# medium is the material of the nanorods\n", "def construct_antenna(L_f, r, lda0, medium):\n", " L_r = L_f * 1.25 # length of the reflector\n", " L_d = L_f * 0.9 # length of the directors\n", " a_r = lda0 / 4.4 # spacing between the feed and the reflector\n", " a_d = (\n", " lda0 / 4\n", " ) # spacing between the feed and the first director (also the spacing between directors)\n", "\n", " feed = [\n", " td.Structure(\n", " geometry=td.Cylinder(\n", " center=(0, 0, 0), radius=r, length=L_f - 2 * r, axis=1\n", " ),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(0, (L_f - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(0, -(L_f - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " ]\n", "\n", " reflector = [\n", " td.Structure(\n", " geometry=td.Cylinder(\n", " center=(-a_r, 0, 0), radius=r, length=L_r - 2 * r, axis=1\n", " ),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(-a_r, (L_r - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(-a_r, -(L_r - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " ]\n", "\n", " director_1 = [\n", " td.Structure(\n", " geometry=td.Cylinder(\n", " center=(a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n", " ),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(a_d, (L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(a_d, -(L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " ]\n", "\n", " director_2 = [\n", " td.Structure(\n", " geometry=td.Cylinder(\n", " center=(2 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n", " ),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(2 * a_d, (L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(2 * a_d, -(L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " ]\n", "\n", " director_3 = [\n", " td.Structure(\n", " geometry=td.Cylinder(\n", " center=(3 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n", " ),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(3 * a_d, (L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " td.Structure(\n", " geometry=td.Sphere(center=(3 * a_d, -(L_d - 2 * r) / 2, 0), radius=r),\n", " medium=medium,\n", " ),\n", " ]\n", "\n", " antenna = feed + reflector + director_1 + director_2 + director_3\n", " return antenna\n", "\n", "\n", "L_f = 0.16 # length of the feed\n", "r = 0.02 # radius of the nanorods\n", "medium = al # material of the antenna\n", "\n", "antenna = construct_antenna(L_f, r, lda0, medium)\n" ] }, { "cell_type": "markdown", "id": "94620334", "metadata": {}, "source": [ "The Yagi-Uda antenna is usually fed by a small quantum emitter such as a laser-excited quantum dot. In the simulation, the source can be well approximated as a [PointDipole](../_autosummary/tidy3d.PointDipole.html), which is what we are going to use.\n", "\n", "To calculate the far-field radiation pattern and directivity, we will use the [FieldProjectionAngleMonitor](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html) as well as a [FluxMonitor](../_autosummary/tidy3d.FluxMonitor.html). The [FieldProjectionAngleMonitor](../_autosummary/tidy3d.FieldProjectionAngleMonitor.html) yields the angular radiation power and the [FluxMonitor](../_autosummary/tidy3d.FluxMonitor.html) helps to calculate the total radiated power. Both are required in the calculation of directivity." ] }, { "cell_type": "code", "execution_count": 5, "id": "79a4c9d5", "metadata": { "execution": { "iopub.execute_input": "2023-03-27T21:13:53.089863Z", "iopub.status.busy": "2023-03-27T21:13:53.089722Z", "iopub.status.idle": "2023-03-27T21:13:53.483183Z", "shell.execute_reply": "2023-03-27T21:13:53.482597Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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[08:56:24] Created task 'plasmonic_yagi_uda' with task_id webapi.py:139\n", " 'fdve-514e0455-e2c9-4846-9fcf-0ccca2ccb881v1'. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:56:24]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'plasmonic_yagi_uda'\u001b[0m with task_id \u001b]8;id=909651;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=262153;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-514e0455-e2c9-4846-9fcf-0ccca2ccb881v1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7be8c95fbd794704807fd3f588813ed1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:56:30] status = queued webapi.py:269\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:56:30]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued \u001b]8;id=47162;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=20354;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#269\u001b\\\u001b[2m269\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[08:56:35] status = preprocess webapi.py:263\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:56:35]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess \u001b]8;id=36261;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=801761;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#263\u001b\\\u001b[2m263\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[08:56:39] Maximum FlexCredit cost: 0.058. Use 'web.real_cost(task_id)' to get the billed FlexCredit cost webapi.py:286\n", " after a simulation run. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:56:39]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.058\u001b[0m. Use \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 FlexCredit cost \u001b]8;id=152461;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=547875;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mafter a simulation run. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
starting up solver webapi.py:290\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mstarting up solver \u001b]8;id=364003;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=339454;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[08:56:40] running solver webapi.py:300\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:56:40]\u001b[0m\u001b[2;36m \u001b[0mrunning solver \u001b]8;id=224770;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=526880;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1dfca6121c594da7ad26af9788123978", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:57:23] loading SimulationData from data/optical_yagi_uda.hdf5 webapi.py:512\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:23]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/optical_yagi_uda.hdf5 \u001b]8;id=626777;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=502913;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[08:57:23] Created task 'empty' with task_id 'fdve-d3f62060-e0f7-40a0-8299-d8a6ff7f2d42v1'. webapi.py:139\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:23]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'empty'\u001b[0m with task_id \u001b[32m'fdve-d3f62060-e0f7-40a0-8299-d8a6ff7f2d42v1'\u001b[0m. \u001b]8;id=100147;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=68818;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f7dcb462702c4751b954918975b3f965", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:57:32] Maximum FlexCredit cost: 0.025. Use 'web.real_cost(task_id)' to get the billed FlexCredit cost webapi.py:286\n", " after a simulation run. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:32]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \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 FlexCredit cost \u001b]8;id=998816;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=25899;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mafter a simulation run. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
starting up solver webapi.py:290\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mstarting up solver \u001b]8;id=614201;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=377562;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[08:57:33] running solver webapi.py:300\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:33]\u001b[0m\u001b[2;36m \u001b[0mrunning solver \u001b]8;id=696029;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=248921;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af5078ad6cf34111a680b223686bbc4f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:57:43] status = success webapi.py:337\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:43]\u001b[0m\u001b[2;36m \u001b[0mstatus = success \u001b]8;id=163264;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=721813;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#337\u001b\\\u001b[2m337\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cda8dadec2b14eb6a61a947b49764ce3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:57:45] loading SimulationData from data/optical_yagi_uda.hdf5 webapi.py:512\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:45]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/optical_yagi_uda.hdf5 \u001b]8;id=720802;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=325795;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim_data = web.run(\n", " sim, task_name=\"plasmonic_yagi_uda\", path=\"data/optical_yagi_uda.hdf5\", verbose=True\n", ")\n", "sim_empty_data = web.run(\n", " sim_empty, task_name=\"empty\", path=\"data/optical_yagi_uda.hdf5\", verbose=True\n", ")\n" ] }, { "cell_type": "markdown", "id": "7a334ffc", "metadata": {}, "source": [ "## Postprocessing" ] }, { "cell_type": "markdown", "id": "d002725e", "metadata": {}, "source": [ "After the simulations are complete, we calculate the directivity of the Yagi-Uda antenna and the single point dipole. The directivity is given by\n", "\n", "
[08:57:47] Created task 'plasmonic_yagi_uda' with task_id webapi.py:139\n", " 'fdve-6cb09279-3da2-4a2b-b5a6-8bf93b6ea112v1'. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:47]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'plasmonic_yagi_uda'\u001b[0m with task_id \u001b]8;id=237107;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=147732;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-6cb09279-3da2-4a2b-b5a6-8bf93b6ea112v1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c0aecb4d074b4d2983eae55e362a5d37", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:57:55] Maximum FlexCredit cost: 0.746. Use 'web.real_cost(task_id)' to get the billed FlexCredit cost webapi.py:286\n", " after a simulation run. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:57:55]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.746\u001b[0m. Use \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 FlexCredit cost \u001b]8;id=552198;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=602653;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mafter a simulation run. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
starting up solver webapi.py:290\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mstarting up solver \u001b]8;id=606271;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=582208;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
running solver webapi.py:300\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mrunning solver \u001b]8;id=494431;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=453472;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#300\u001b\\\u001b[2m300\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "842bf193845844a79bac5776fc8b2b3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:58:35] loading SimulationData from data/optical_yagi_uda.hdf5 webapi.py:512\n", "\n" ], "text/plain": [ "\u001b[2;36m[08:58:35]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/optical_yagi_uda.hdf5 \u001b]8;id=567189;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=432443;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim_data = web.run(\n", " sim, task_name=\"plasmonic_yagi_uda\", path=\"data/optical_yagi_uda.hdf5\", verbose=True\n", ")\n" ] }, { "cell_type": "markdown", "id": "9fb88e64", "metadata": {}, "source": [ "In this simulation, the way to calculate the directivity is a little different. The simulation domain size is 15$\\lambda$. We can evaluate the radiated field at a circle 7$\\lambda$ away from the antenna. At this distance, the near field should completely decay away so the field is purely radiated field. Therefore, the power is directly given by\n", "\n", "
[08:58:41] Created task 'plasmonic_yagi_uda_on_glass' with task_id webapi.py:139\n", " 'fdve-42ae19c6-f252-4bb2-b1ce-ef4b2f1a8df3v1'. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:58:41]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'plasmonic_yagi_uda_on_glass'\u001b[0m with task_id \u001b]8;id=538042;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=363499;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#139\u001b\\\u001b[2m139\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-42ae19c6-f252-4bb2-b1ce-ef4b2f1a8df3v1'\u001b[0m. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0dd908b094bd4cfd8c8f676e9e7fc302", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[08:59:49] Maximum FlexCredit cost: 3.070. Use 'web.real_cost(task_id)' to get the billed FlexCredit cost webapi.py:286\n", " after a simulation run. \n", "\n" ], "text/plain": [ "\u001b[2;36m[08:59:49]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m3.070\u001b[0m. Use \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 FlexCredit cost \u001b]8;id=749064;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=127433;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0mafter a simulation run. \u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
starting up solver webapi.py:290\n", "\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mstarting up solver \u001b]8;id=496052;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=340821;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#290\u001b\\\u001b[2m290\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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[09:01:59] loading SimulationData from data/optical_yagi_uda.hdf5 webapi.py:512\n", "\n" ], "text/plain": [ "\u001b[2;36m[09:01:59]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/optical_yagi_uda.hdf5 \u001b]8;id=883770;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=129146;file://C:\\Users\\xinzhong\\Desktop\\tidy3d\\tidy3d\\web\\webapi.py#512\u001b\\\u001b[2m512\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim_data = web.run(\n", " sim,\n", " task_name=\"plasmonic_yagi_uda_on_glass\",\n", " path=\"data/optical_yagi_uda.hdf5\",\n", " verbose=True,\n", ")\n" ] }, { "cell_type": "markdown", "id": "6918092c", "metadata": {}, "source": [ "After the simulation is complete, we calculate the directivity in the same way as previously. Note that the intrinsic impedance $\\eta=\\eta_0/n$ is different in the glass ($n=1.5$) compared to in the free space ($n=1$). \n", "\n", "From the directivity plot, we can see that a directivity close to 25 can be achieved in this case." ] }, { "cell_type": "code", "execution_count": 16, "id": "210e80f0", "metadata": { "execution": { "iopub.execute_input": "2023-03-27T21:20:58.992212Z", "iopub.status.busy": "2023-03-27T21:20:58.992008Z", "iopub.status.idle": "2023-03-27T21:21:07.062476Z", "shell.execute_reply": "2023-03-27T21:21:07.061722Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
\ud83c\udfc3 Finishing 'plasmonic_yagi_uda_on_glass'...\n\n", "text/plain": "\u001b[32m\ud83c\udfc3 \u001b[0m \u001b[1;32mFinishing 'plasmonic_yagi_uda_on_glass'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0957eb54ab4d46e792a0e0107b22ebee": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_cb08da629b354078a68488c60721b5d6", "msg_id": "", "outputs": [ { "data": { "text/html": "
\ud83c\udfc3 Finishing 'plasmonic_yagi_uda'...\n\n", "text/plain": "\u001b[32m\ud83c\udfc3 \u001b[0m \u001b[1;32mFinishing 'plasmonic_yagi_uda'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "096c3b14f5ff44418bd4d1044a0ef752": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_f55086fc2c4849bf968f9478c524971f", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 4.63e-06) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\n\n", "text/plain": "% done (field decay = 4.63e-06) \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0ab235d78e38429b80e2979a6d703497": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_7a23f8b190dc4d33bed26a04c4958220", "msg_id": "", "outputs": [ { "data": { "text/html": "
best RMS error so far: 3.84e-02 \u2501\u2501\u2501\u257a\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 8% 0:01:12\n\n", "text/plain": "best RMS error so far: 3.84e-02 \u001b[38;2;249;38;114m\u2501\u2501\u2501\u001b[0m\u001b[38;5;237m\u257a\u001b[0m\u001b[38;5;237m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m 8%\u001b[0m \u001b[36m0:01:12\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "0d5b91c730954e898a608ed687f8955b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_337bb19609634fbfa4ce6367b3d4ab31", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 0.00e+00) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\n\n", "text/plain": "% done (field decay = 0.00e+00) \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "1a0943dbb38b41edb8ab394df296742a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1c162092f346448aa52e81970aa092b3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "24cf8aeb2282458290c3e21212a234af": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "31508fb7f73241748f95a0a04f2e83de": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_9daeb294ff214676963694a4f8ff38bc", "msg_id": "", "outputs": [ { "data": { "text/html": "
\u2191 simulation.json \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100.0% \u2022 10.8/10.8 kB \u2022 ? \u2022 0:00:00\n\n", "text/plain": "\u001b[1;31m\u2191\u001b[0m \u001b[1;34msimulation.json\u001b[0m \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100.0%\u001b[0m \u2022 \u001b[32m10.8/10.8 kB\u001b[0m \u2022 \u001b[31m?\u001b[0m \u2022 \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "337bb19609634fbfa4ce6367b3d4ab31": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3e24f5c29cb844a8ac76df877530e808": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "442bfa3bfba344408c1cb6c7a3f733d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "451845c35f6b47a4ac1f65d72cc41673": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_83995eb6e1174839ab553beb2bdfbd44", "msg_id": "", "outputs": [ { "data": { "text/html": "
\ud83c\udfc3 Finishing 'plasmonic_yagi_uda'...\n\n", "text/plain": "\u001b[32m\ud83c\udfc3 \u001b[0m \u001b[1;32mFinishing 'plasmonic_yagi_uda'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "46f0eec361054993baa54848604285e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "548091c6cf9b4f0fb156feb8da30b3e6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_24cf8aeb2282458290c3e21212a234af", "msg_id": "", "outputs": [ { "data": { "text/html": "
% done (field decay = 0.00e+00) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 100% 0:00:00\n\n", "text/plain": "% done (field decay = 0.00e+00) \u001b[38;2;114;156;31m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[35m100%\u001b[0m \u001b[36m0:00:00\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "55687f4e030a40dba4ceffc8d3b57a71": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "65dac6396879442d98ce5ebe733134c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6982a6dc08df4f908268748bfe7ceefb": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_be77fab4a88345ecb1e5f40254955ad5", "msg_id": "", "outputs": [ { "data": { "text/html": "
\ud83c\udfc3 Starting 'plasmonic_yagi_uda'...\n\n", "text/plain": "\u001b[32m\ud83c\udfc3 \u001b[0m \u001b[1;32mStarting 'plasmonic_yagi_uda'...\u001b[0m\n" }, "metadata": {}, "output_type": "display_data" } ] } }, "704b0aec4ffa458a8020ad986ac6822f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "76ae7691c8c642b7bbc67feaa5a327be": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_3e24f5c29cb844a8ac76df877530e808", "msg_id": "", "outputs": [ { "data": { "text/html": "
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