{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Directional Coupler and Geometry Objects from GDSII File\n", "\n", "The directional coupler as well as the source and mode monitor geometries are described by the GDSII file [examples/coupler.gds](https://github.com/NanoComp/meep/blob/master/python/examples/coupler.gds). A snapshot of this file viewed using [KLayout](https://www.klayout.de/) is shown below. The figure labels have been added in post processing. The design consists of two identical strip waveguides which are positioned close together via an adiabatic taper such that their modes couple evanescently. There is a source (labelled \"Source\") and four mode monitors (labelled \"Port 1\", etc.). The input pulse from Port 1 is split in two and exits through Ports 3 and 4. The design objective is to find the separation distance (labelled \"d\") which maximizes power in Port 4 at a wavelength of 1.55 μm. More generally, though not included in this example, it is possible to have two additional degrees of freedom: (1) the length of the straight waveguide section where the two waveguides are coupled and (2) the length of the tapered section (the taper profile is described by a hyperbolic tangent (tanh) function).\n", "\n", "![](https://meep.readthedocs.io/en/latest/images/klayout_schematic.png)\n", "\n", "The GDSII file is adapted from the [SiEPIC EBeam PDK](https://github.com/lukasc-ubc/SiEPIC_EBeam_PDK) with four major modifications:\n", "\n", "+ the computational cell is centered at the origin of the *xy* plane and defined on layer 0\n", "\n", "+ the source and four mode monitors are defined on layers 1-5\n", "\n", "+ the lower and upper branches of the coupler are defined on layers 31 and 32\n", "\n", "+ the straight waveguide sections are perfectly linear\n", "\n", "Note that rather than being specified as part of the GDSII file, the volume regions of the source and flux monitors could have been specified in the simulation script." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------\n", "Initializing structure...\n", "time for choose_chunkdivision = 0.000570059 s\n", "Working in 2D dimensions.\n", "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", " prism, center = (-9.09425,1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (-4,0.06,-10)\n", " (-4.108,0.061,-10)\n", " (-4.215,0.062,-10)\n", " (-4.322,0.065,-10)\n", " (-4.429,0.07,-10)\n", " (-4.535,0.075,-10)\n", " (-4.641,0.081,-10)\n", " (-4.747,0.089,-10)\n", " (-4.852,0.097,-10)\n", " (-5.062,0.117,-10)\n", " (-5.167,0.129,-10)\n", " (-5.271,0.141,-10)\n", " (-5.479,0.169,-10)\n", " (-5.582,0.184,-10)\n", " (-5.685,0.2,-10)\n", " (-5.788,0.217,-10)\n", " (-5.891,0.235,-10)\n", " (-5.993,0.253,-10)\n", " (-6.095,0.272,-10)\n", " (-6.197,0.292,-10)\n", " (-6.299,0.313,-10)\n", " (-6.4,0.334,-10)\n", " (-6.501,0.356,-10)\n", " (-6.703,0.402,-10)\n", " (-6.803,0.425,-10)\n", " (-6.904,0.45,-10)\n", " (-7.204,0.525,-10)\n", " (-7.303,0.552,-10)\n", " (-7.403,0.578,-10)\n", " (-7.502,0.605,-10)\n", " (-7.601,0.633,-10)\n", " (-7.7,0.66,-10)\n", " (-7.799,0.688,-10)\n", " (-7.898,0.717,-10)\n", " (-7.996,0.745,-10)\n", " (-8.095,0.774,-10)\n", " (-8.193,0.803,-10)\n", " (-8.291,0.833,-10)\n", " (-8.389,0.862,-10)\n", " (-8.487,0.892,-10)\n", " (-8.585,0.921,-10)\n", " (-8.781,0.981,-10)\n", " (-8.878,1.011,-10)\n", " (-9.074,1.071,-10)\n", " (-9.268,1.131,-10)\n", " (-9.366,1.161,-10)\n", " (-9.463,1.191,-10)\n", " (-9.56,1.22,-10)\n", " (-9.658,1.25,-10)\n", " (-9.949,1.337,-10)\n", " (-10.047,1.366,-10)\n", " (-10.144,1.395,-10)\n", " (-10.338,1.451,-10)\n", " (-10.436,1.478,-10)\n", " (-10.533,1.506,-10)\n", " (-10.63,1.533,-10)\n", " (-10.728,1.559,-10)\n", " (-10.922,1.611,-10)\n", " (-11.02,1.636,-10)\n", " (-11.117,1.66,-10)\n", " (-11.215,1.685,-10)\n", " (-11.313,1.708,-10)\n", " (-11.41,1.731,-10)\n", " (-11.508,1.754,-10)\n", " (-11.606,1.776,-10)\n", " (-11.704,1.797,-10)\n", " (-11.802,1.817,-10)\n", " (-11.901,1.837,-10)\n", " (-11.999,1.856,-10)\n", " (-12.098,1.875,-10)\n", " (-12.196,1.893,-10)\n", " (-12.295,1.91,-10)\n", " (-12.394,1.926,-10)\n", " (-12.592,1.956,-10)\n", " (-12.691,1.97,-10)\n", " (-12.791,1.982,-10)\n", " (-12.89,1.994,-10)\n", " (-12.99,2.005,-10)\n", " (-13.19,2.025,-10)\n", " (-13.291,2.033,-10)\n", " (-13.392,2.04,-10)\n", " (-13.492,2.046,-10)\n", " (-13.593,2.051,-10)\n", " (-13.695,2.055,-10)\n", " (-13.796,2.058,-10)\n", " (-14,2.06,-10)\n", " (-17.2,2.06,-10)\n", " (-17.2,2.56,-10)\n", " (-14,2.56,-10)\n", " (-13.892,2.559,-10)\n", " (-13.785,2.558,-10)\n", " (-13.678,2.555,-10)\n", " (-13.571,2.55,-10)\n", " (-13.465,2.545,-10)\n", " (-13.359,2.539,-10)\n", " (-13.253,2.531,-10)\n", " (-13.148,2.523,-10)\n", " (-12.938,2.503,-10)\n", " (-12.833,2.491,-10)\n", " (-12.729,2.479,-10)\n", " (-12.521,2.451,-10)\n", " (-12.418,2.436,-10)\n", " (-12.315,2.42,-10)\n", " (-12.212,2.403,-10)\n", " (-12.109,2.385,-10)\n", " (-12.007,2.367,-10)\n", " (-11.905,2.348,-10)\n", " (-11.803,2.328,-10)\n", " (-11.701,2.307,-10)\n", " (-11.6,2.286,-10)\n", " (-11.499,2.264,-10)\n", " (-11.297,2.218,-10)\n", " (-11.197,2.195,-10)\n", " (-11.096,2.17,-10)\n", " (-10.796,2.095,-10)\n", " (-10.697,2.068,-10)\n", " (-10.597,2.042,-10)\n", " (-10.498,2.015,-10)\n", " (-10.399,1.987,-10)\n", " (-10.3,1.96,-10)\n", " (-10.201,1.932,-10)\n", " (-10.102,1.903,-10)\n", " (-10.004,1.875,-10)\n", " (-9.905,1.846,-10)\n", " (-9.807,1.817,-10)\n", " (-9.709,1.787,-10)\n", " (-9.611,1.758,-10)\n", " (-9.513,1.728,-10)\n", " (-9.415,1.699,-10)\n", " (-9.219,1.639,-10)\n", " (-9.122,1.609,-10)\n", " (-8.926,1.549,-10)\n", " (-8.732,1.489,-10)\n", " (-8.634,1.459,-10)\n", " (-8.537,1.429,-10)\n", " (-8.44,1.4,-10)\n", " (-8.342,1.37,-10)\n", " (-8.051,1.283,-10)\n", " (-7.953,1.254,-10)\n", " (-7.856,1.225,-10)\n", " (-7.662,1.169,-10)\n", " (-7.564,1.142,-10)\n", " (-7.467,1.114,-10)\n", " (-7.37,1.087,-10)\n", " (-7.272,1.061,-10)\n", " (-7.078,1.009,-10)\n", " (-6.98,0.984,-10)\n", " (-6.883,0.96,-10)\n", " (-6.785,0.935,-10)\n", " (-6.687,0.912,-10)\n", " (-6.59,0.889,-10)\n", " (-6.492,0.866,-10)\n", " (-6.394,0.844,-10)\n", " (-6.296,0.823,-10)\n", " (-6.198,0.803,-10)\n", " (-6.099,0.783,-10)\n", " (-6.001,0.764,-10)\n", " (-5.902,0.745,-10)\n", " (-5.804,0.727,-10)\n", " (-5.705,0.71,-10)\n", " (-5.606,0.694,-10)\n", " (-5.408,0.664,-10)\n", " (-5.309,0.65,-10)\n", " (-5.209,0.638,-10)\n", " (-5.11,0.626,-10)\n", " (-5.01,0.615,-10)\n", " (-4.81,0.595,-10)\n", " (-4.709,0.587,-10)\n", " (-4.608,0.58,-10)\n", " (-4.508,0.574,-10)\n", " (-4.407,0.569,-10)\n", " (-4.305,0.565,-10)\n", " (-4.204,0.562,-10)\n", " (-4,0.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (4,0.06,-10)\n", " (4,0.56,-10)\n", " (4.204,0.562,-10)\n", " (4.305,0.565,-10)\n", " (4.407,0.569,-10)\n", " (4.508,0.574,-10)\n", " (4.608,0.58,-10)\n", " (4.709,0.587,-10)\n", " (4.81,0.595,-10)\n", " (5.01,0.615,-10)\n", " (5.11,0.626,-10)\n", " (5.209,0.638,-10)\n", " (5.309,0.65,-10)\n", " (5.408,0.664,-10)\n", " (5.606,0.694,-10)\n", " (5.705,0.71,-10)\n", " (5.804,0.727,-10)\n", " (5.902,0.745,-10)\n", " (6.001,0.764,-10)\n", " (6.099,0.783,-10)\n", " (6.198,0.803,-10)\n", " (6.296,0.823,-10)\n", " (6.394,0.844,-10)\n", " (6.492,0.866,-10)\n", " (6.59,0.889,-10)\n", " (6.687,0.912,-10)\n", " (6.785,0.935,-10)\n", " (6.883,0.96,-10)\n", " (6.98,0.984,-10)\n", " (7.078,1.009,-10)\n", " (7.272,1.061,-10)\n", " (7.37,1.087,-10)\n", " (7.467,1.114,-10)\n", " (7.564,1.142,-10)\n", " (7.662,1.169,-10)\n", " (7.856,1.225,-10)\n", " (7.953,1.254,-10)\n", " (8.051,1.283,-10)\n", " (8.342,1.37,-10)\n", " (8.44,1.4,-10)\n", " (8.537,1.429,-10)\n", " (8.634,1.459,-10)\n", " (8.732,1.489,-10)\n", " (8.926,1.549,-10)\n", " (9.122,1.609,-10)\n", " (9.219,1.639,-10)\n", " (9.415,1.699,-10)\n", " (9.513,1.728,-10)\n", " (9.611,1.758,-10)\n", " (9.709,1.787,-10)\n", " (9.807,1.817,-10)\n", " (9.905,1.846,-10)\n", " (10.004,1.875,-10)\n", " (10.102,1.903,-10)\n", " (10.201,1.932,-10)\n", " (10.3,1.96,-10)\n", " (10.399,1.987,-10)\n", " (10.498,2.015,-10)\n", " (10.597,2.042,-10)\n", " (10.697,2.068,-10)\n", " (10.796,2.095,-10)\n", " (11.096,2.17,-10)\n", " (11.197,2.195,-10)\n", " (11.297,2.218,-10)\n", " (11.499,2.264,-10)\n", " (11.6,2.286,-10)\n", " (11.701,2.307,-10)\n", " (11.803,2.328,-10)\n", " (11.905,2.348,-10)\n", " (12.007,2.367,-10)\n", " (12.109,2.385,-10)\n", " (12.212,2.403,-10)\n", " (12.315,2.42,-10)\n", " (12.418,2.436,-10)\n", " (12.521,2.451,-10)\n", " (12.729,2.479,-10)\n", " (12.833,2.491,-10)\n", " (12.938,2.503,-10)\n", " (13.148,2.523,-10)\n", " (13.253,2.531,-10)\n", " (13.359,2.539,-10)\n", " (13.465,2.545,-10)\n", " (13.571,2.55,-10)\n", " (13.678,2.555,-10)\n", " (13.785,2.558,-10)\n", " (13.892,2.559,-10)\n", " (14,2.56,-10)\n", " (17.2,2.56,-10)\n", " (17.2,2.06,-10)\n", " (14,2.06,-10)\n", " (13.796,2.058,-10)\n", " (13.695,2.055,-10)\n", " (13.593,2.051,-10)\n", " (13.492,2.046,-10)\n", " (13.392,2.04,-10)\n", " (13.291,2.033,-10)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (13.19,2.025,-10)\n", " (12.99,2.005,-10)\n", " (12.89,1.994,-10)\n", " (12.791,1.982,-10)\n", " (12.691,1.97,-10)\n", " (12.592,1.956,-10)\n", " (12.394,1.926,-10)\n", " (12.295,1.91,-10)\n", " (12.196,1.893,-10)\n", " (12.098,1.875,-10)\n", " (11.999,1.856,-10)\n", " (11.901,1.837,-10)\n", " (11.802,1.817,-10)\n", " (11.704,1.797,-10)\n", " (11.606,1.776,-10)\n", " (11.508,1.754,-10)\n", " (11.41,1.731,-10)\n", " (11.313,1.708,-10)\n", " (11.215,1.685,-10)\n", " (11.117,1.66,-10)\n", " (11.02,1.636,-10)\n", " (10.922,1.611,-10)\n", " (10.728,1.559,-10)\n", " (10.63,1.533,-10)\n", " (10.533,1.506,-10)\n", " (10.436,1.478,-10)\n", " (10.338,1.451,-10)\n", " (10.144,1.395,-10)\n", " (10.047,1.366,-10)\n", " (9.949,1.337,-10)\n", " (9.658,1.25,-10)\n", " (9.56,1.22,-10)\n", " (9.463,1.191,-10)\n", " (9.366,1.161,-10)\n", " (9.268,1.131,-10)\n", " (9.074,1.071,-10)\n", " (8.878,1.011,-10)\n", " (8.781,0.981,-10)\n", " (8.585,0.921,-10)\n", " (8.487,0.892,-10)\n", " (8.389,0.862,-10)\n", " (8.291,0.833,-10)\n", " (8.193,0.803,-10)\n", " (8.095,0.774,-10)\n", " (7.996,0.745,-10)\n", " (7.898,0.717,-10)\n", " (7.799,0.688,-10)\n", " (7.7,0.66,-10)\n", " (7.601,0.633,-10)\n", " (7.502,0.605,-10)\n", " (7.403,0.578,-10)\n", " (7.303,0.552,-10)\n", " (7.204,0.525,-10)\n", " (6.904,0.45,-10)\n", " (6.803,0.425,-10)\n", " (6.703,0.402,-10)\n", " (6.501,0.356,-10)\n", " (6.4,0.334,-10)\n", " (6.299,0.313,-10)\n", " (6.197,0.292,-10)\n", " (6.095,0.272,-10)\n", " (5.993,0.253,-10)\n", " (5.891,0.235,-10)\n", " (5.788,0.217,-10)\n", " (5.685,0.2,-10)\n", " (5.582,0.184,-10)\n", " (5.479,0.169,-10)\n", " (5.271,0.141,-10)\n", " (5.167,0.129,-10)\n", " (5.062,0.117,-10)\n", " (4.852,0.097,-10)\n", " (4.747,0.089,-10)\n", " (4.641,0.081,-10)\n", " (4.535,0.075,-10)\n", " (4.429,0.07,-10)\n", " (4.322,0.065,-10)\n", " (4.215,0.062,-10)\n", " (4.108,0.061,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,0.31,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", " (-4,0.06,-10)\n", " (-4,0.56,-10)\n", " (4,0.56,-10)\n", " (4,0.06,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (-9.09425,-1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (-17.2,-2.56,-10)\n", " (-17.2,-2.06,-10)\n", " (-14,-2.06,-10)\n", " (-13.796,-2.058,-10)\n", " (-13.695,-2.055,-10)\n", " (-13.593,-2.051,-10)\n", " (-13.492,-2.046,-10)\n", " (-13.392,-2.04,-10)\n", " (-13.291,-2.033,-10)\n", " (-13.19,-2.025,-10)\n", " (-12.99,-2.005,-10)\n", " (-12.89,-1.994,-10)\n", " (-12.791,-1.982,-10)\n", " (-12.691,-1.97,-10)\n", " (-12.592,-1.956,-10)\n", " (-12.394,-1.926,-10)\n", " (-12.295,-1.91,-10)\n", " (-12.196,-1.893,-10)\n", " (-12.098,-1.875,-10)\n", " (-11.999,-1.856,-10)\n", " (-11.901,-1.837,-10)\n", " (-11.802,-1.817,-10)\n", " (-11.704,-1.797,-10)\n", " (-11.606,-1.776,-10)\n", " (-11.508,-1.754,-10)\n", " (-11.41,-1.731,-10)\n", " (-11.313,-1.708,-10)\n", " (-11.215,-1.685,-10)\n", " (-11.117,-1.66,-10)\n", " (-11.02,-1.636,-10)\n", " (-10.922,-1.611,-10)\n", " (-10.728,-1.559,-10)\n", " (-10.63,-1.533,-10)\n", " (-10.533,-1.506,-10)\n", " (-10.436,-1.478,-10)\n", " (-10.338,-1.451,-10)\n", " (-10.144,-1.395,-10)\n", " (-10.047,-1.366,-10)\n", " (-9.949,-1.337,-10)\n", " (-9.658,-1.25,-10)\n", " (-9.56,-1.22,-10)\n", " (-9.463,-1.191,-10)\n", " (-9.366,-1.161,-10)\n", " (-9.268,-1.131,-10)\n", " (-9.074,-1.071,-10)\n", " (-8.878,-1.011,-10)\n", " (-8.781,-0.981,-10)\n", " (-8.585,-0.921,-10)\n", " (-8.487,-0.892,-10)\n", " (-8.389,-0.862,-10)\n", " (-8.291,-0.833,-10)\n", " (-8.193,-0.803,-10)\n", " (-8.095,-0.774,-10)\n", " (-7.996,-0.745,-10)\n", " (-7.898,-0.717,-10)\n", " (-7.799,-0.688,-10)\n", " (-7.7,-0.66,-10)\n", " (-7.601,-0.633,-10)\n", " (-7.502,-0.605,-10)\n", " (-7.403,-0.578,-10)\n", " (-7.303,-0.552,-10)\n", " (-7.204,-0.525,-10)\n", " (-6.904,-0.45,-10)\n", " (-6.803,-0.425,-10)\n", " (-6.703,-0.402,-10)\n", " (-6.501,-0.356,-10)\n", " (-6.4,-0.334,-10)\n", " (-6.299,-0.313,-10)\n", " (-6.197,-0.292,-10)\n", " (-6.095,-0.272,-10)\n", " (-5.993,-0.253,-10)\n", " (-5.891,-0.235,-10)\n", " (-5.788,-0.217,-10)\n", " (-5.685,-0.2,-10)\n", " (-5.582,-0.184,-10)\n", " (-5.479,-0.169,-10)\n", " (-5.271,-0.141,-10)\n", " (-5.167,-0.129,-10)\n", " (-5.062,-0.117,-10)\n", " (-4.852,-0.097,-10)\n", " (-4.747,-0.089,-10)\n", " (-4.641,-0.081,-10)\n", " (-4.535,-0.075,-10)\n", " (-4.429,-0.07,-10)\n", " (-4.322,-0.065,-10)\n", " (-4.215,-0.062,-10)\n", " (-4.108,-0.061,-10)\n", " (-4,-0.06,-10)\n", " (-4,-0.56,-10)\n", " (-4.204,-0.562,-10)\n", " (-4.305,-0.565,-10)\n", " (-4.407,-0.569,-10)\n", " (-4.508,-0.574,-10)\n", " (-4.608,-0.58,-10)\n", " (-4.709,-0.587,-10)\n", " (-4.81,-0.595,-10)\n", " (-5.01,-0.615,-10)\n", " (-5.11,-0.626,-10)\n", " (-5.209,-0.638,-10)\n", " (-5.309,-0.65,-10)\n", " (-5.408,-0.664,-10)\n", " (-5.606,-0.694,-10)\n", " (-5.705,-0.71,-10)\n", " (-5.804,-0.727,-10)\n", " (-5.902,-0.745,-10)\n", " (-6.001,-0.764,-10)\n", " (-6.099,-0.783,-10)\n", " (-6.198,-0.803,-10)\n", " (-6.296,-0.823,-10)\n", " (-6.394,-0.844,-10)\n", " (-6.492,-0.866,-10)\n", " (-6.59,-0.889,-10)\n", " (-6.687,-0.912,-10)\n", " (-6.785,-0.935,-10)\n", " (-6.883,-0.96,-10)\n", " (-6.98,-0.984,-10)\n", " (-7.078,-1.009,-10)\n", " (-7.272,-1.061,-10)\n", " (-7.37,-1.087,-10)\n", " (-7.467,-1.114,-10)\n", " (-7.564,-1.142,-10)\n", " (-7.662,-1.169,-10)\n", " (-7.856,-1.225,-10)\n", " (-7.953,-1.254,-10)\n", " (-8.051,-1.283,-10)\n", " (-8.342,-1.37,-10)\n", " (-8.44,-1.4,-10)\n", " (-8.537,-1.429,-10)\n", " (-8.634,-1.459,-10)\n", " (-8.732,-1.489,-10)\n", " (-8.926,-1.549,-10)\n", " (-9.122,-1.609,-10)\n", " (-9.219,-1.639,-10)\n", " (-9.415,-1.699,-10)\n", " (-9.513,-1.728,-10)\n", " (-9.611,-1.758,-10)\n", " (-9.709,-1.787,-10)\n", " (-9.807,-1.817,-10)\n", " (-9.905,-1.846,-10)\n", " (-10.004,-1.875,-10)\n", " (-10.102,-1.903,-10)\n", " (-10.201,-1.932,-10)\n", " (-10.3,-1.96,-10)\n", " (-10.399,-1.987,-10)\n", " (-10.498,-2.015,-10)\n", " (-10.597,-2.042,-10)\n", " (-10.697,-2.068,-10)\n", " (-10.796,-2.095,-10)\n", " (-11.096,-2.17,-10)\n", " (-11.197,-2.195,-10)\n", " (-11.297,-2.218,-10)\n", " (-11.499,-2.264,-10)\n", " (-11.6,-2.286,-10)\n", " (-11.701,-2.307,-10)\n", " (-11.803,-2.328,-10)\n", " (-11.905,-2.348,-10)\n", " (-12.007,-2.367,-10)\n", " (-12.109,-2.385,-10)\n", " (-12.212,-2.403,-10)\n", " (-12.315,-2.42,-10)\n", " (-12.418,-2.436,-10)\n", " (-12.521,-2.451,-10)\n", " (-12.729,-2.479,-10)\n", " (-12.833,-2.491,-10)\n", " (-12.938,-2.503,-10)\n", " (-13.148,-2.523,-10)\n", " (-13.253,-2.531,-10)\n", " (-13.359,-2.539,-10)\n", " (-13.465,-2.545,-10)\n", " (-13.571,-2.55,-10)\n", " (-13.678,-2.555,-10)\n", " (-13.785,-2.558,-10)\n", " (-13.892,-2.559,-10)\n", " (-14,-2.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,-1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (14,-2.56,-10)\n", " (13.892,-2.559,-10)\n", " (13.785,-2.558,-10)\n", " (13.678,-2.555,-10)\n", " (13.571,-2.55,-10)\n", " (13.465,-2.545,-10)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (13.359,-2.539,-10)\n", " (13.253,-2.531,-10)\n", " (13.148,-2.523,-10)\n", " (12.938,-2.503,-10)\n", " (12.833,-2.491,-10)\n", " (12.729,-2.479,-10)\n", " (12.521,-2.451,-10)\n", " (12.418,-2.436,-10)\n", " (12.315,-2.42,-10)\n", " (12.212,-2.403,-10)\n", " (12.109,-2.385,-10)\n", " (12.007,-2.367,-10)\n", " (11.905,-2.348,-10)\n", " (11.803,-2.328,-10)\n", " (11.701,-2.307,-10)\n", " (11.6,-2.286,-10)\n", " (11.499,-2.264,-10)\n", " (11.297,-2.218,-10)\n", " (11.197,-2.195,-10)\n", " (11.096,-2.17,-10)\n", " (10.796,-2.095,-10)\n", " (10.697,-2.068,-10)\n", " (10.597,-2.042,-10)\n", " (10.498,-2.015,-10)\n", " (10.399,-1.987,-10)\n", " (10.3,-1.96,-10)\n", " (10.201,-1.932,-10)\n", " (10.102,-1.903,-10)\n", " (10.004,-1.875,-10)\n", " (9.905,-1.846,-10)\n", " (9.807,-1.817,-10)\n", " (9.709,-1.787,-10)\n", " (9.611,-1.758,-10)\n", " (9.513,-1.728,-10)\n", " (9.415,-1.699,-10)\n", " (9.219,-1.639,-10)\n", " (9.122,-1.609,-10)\n", " (8.926,-1.549,-10)\n", " (8.732,-1.489,-10)\n", " (8.634,-1.459,-10)\n", " (8.537,-1.429,-10)\n", " (8.44,-1.4,-10)\n", " (8.342,-1.37,-10)\n", " (8.051,-1.283,-10)\n", " (7.953,-1.254,-10)\n", " (7.856,-1.225,-10)\n", " (7.662,-1.169,-10)\n", " (7.564,-1.142,-10)\n", " (7.467,-1.114,-10)\n", " (7.37,-1.087,-10)\n", " (7.272,-1.061,-10)\n", " (7.078,-1.009,-10)\n", " (6.98,-0.984,-10)\n", " (6.883,-0.96,-10)\n", " (6.785,-0.935,-10)\n", " (6.687,-0.912,-10)\n", " (6.59,-0.889,-10)\n", " (6.492,-0.866,-10)\n", " (6.394,-0.844,-10)\n", " (6.296,-0.823,-10)\n", " (6.198,-0.803,-10)\n", " (6.099,-0.783,-10)\n", " (6.001,-0.764,-10)\n", " (5.902,-0.745,-10)\n", " (5.804,-0.727,-10)\n", " (5.705,-0.71,-10)\n", " (5.606,-0.694,-10)\n", " (5.408,-0.664,-10)\n", " (5.309,-0.65,-10)\n", " (5.209,-0.638,-10)\n", " (5.11,-0.626,-10)\n", " (5.01,-0.615,-10)\n", " (4.81,-0.595,-10)\n", " (4.709,-0.587,-10)\n", " (4.608,-0.58,-10)\n", " (4.508,-0.574,-10)\n", " (4.407,-0.569,-10)\n", " (4.305,-0.565,-10)\n", " (4.204,-0.562,-10)\n", " (4,-0.56,-10)\n", " (4,-0.06,-10)\n", " (4.108,-0.061,-10)\n", " (4.215,-0.062,-10)\n", " (4.322,-0.065,-10)\n", " (4.429,-0.07,-10)\n", " (4.535,-0.075,-10)\n", " (4.641,-0.081,-10)\n", " (4.747,-0.089,-10)\n", " (4.852,-0.097,-10)\n", " (5.062,-0.117,-10)\n", " (5.167,-0.129,-10)\n", " (5.271,-0.141,-10)\n", " (5.479,-0.169,-10)\n", " (5.582,-0.184,-10)\n", " (5.685,-0.2,-10)\n", " (5.788,-0.217,-10)\n", " (5.891,-0.235,-10)\n", " (5.993,-0.253,-10)\n", " (6.095,-0.272,-10)\n", " (6.197,-0.292,-10)\n", " (6.299,-0.313,-10)\n", " (6.4,-0.334,-10)\n", " (6.501,-0.356,-10)\n", " (6.703,-0.402,-10)\n", " (6.803,-0.425,-10)\n", " (6.904,-0.45,-10)\n", " (7.204,-0.525,-10)\n", " (7.303,-0.552,-10)\n", " (7.403,-0.578,-10)\n", " (7.502,-0.605,-10)\n", " (7.601,-0.633,-10)\n", " (7.7,-0.66,-10)\n", " (7.799,-0.688,-10)\n", " (7.898,-0.717,-10)\n", " (7.996,-0.745,-10)\n", " (8.095,-0.774,-10)\n", " (8.193,-0.803,-10)\n", " (8.291,-0.833,-10)\n", " (8.389,-0.862,-10)\n", " (8.487,-0.892,-10)\n", " (8.585,-0.921,-10)\n", " (8.781,-0.981,-10)\n", " (8.878,-1.011,-10)\n", " (9.074,-1.071,-10)\n", " (9.268,-1.131,-10)\n", " (9.366,-1.161,-10)\n", " (9.463,-1.191,-10)\n", " (9.56,-1.22,-10)\n", " (9.658,-1.25,-10)\n", " (9.949,-1.337,-10)\n", " (10.047,-1.366,-10)\n", " (10.144,-1.395,-10)\n", " (10.338,-1.451,-10)\n", " (10.436,-1.478,-10)\n", " (10.533,-1.506,-10)\n", " (10.63,-1.533,-10)\n", " (10.728,-1.559,-10)\n", " (10.922,-1.611,-10)\n", " (11.02,-1.636,-10)\n", " (11.117,-1.66,-10)\n", " (11.215,-1.685,-10)\n", " (11.313,-1.708,-10)\n", " (11.41,-1.731,-10)\n", " (11.508,-1.754,-10)\n", " (11.606,-1.776,-10)\n", " (11.704,-1.797,-10)\n", " (11.802,-1.817,-10)\n", " (11.901,-1.837,-10)\n", " (11.999,-1.856,-10)\n", " (12.098,-1.875,-10)\n", " (12.196,-1.893,-10)\n", " (12.295,-1.91,-10)\n", " (12.394,-1.926,-10)\n", " (12.592,-1.956,-10)\n", " (12.691,-1.97,-10)\n", " (12.791,-1.982,-10)\n", " (12.89,-1.994,-10)\n", " (12.99,-2.005,-10)\n", " (13.19,-2.025,-10)\n", " (13.291,-2.033,-10)\n", " (13.392,-2.04,-10)\n", " (13.492,-2.046,-10)\n", " (13.593,-2.051,-10)\n", " (13.695,-2.055,-10)\n", " (13.796,-2.058,-10)\n", " (14,-2.06,-10)\n", " (17.2,-2.06,-10)\n", " (17.2,-2.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,-0.31,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", " (-4,-0.56,-10)\n", " (-4,-0.06,-10)\n", " (4,-0.06,-10)\n", " (4,-0.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", "subpixel-averaging is 6.81898% done, 58.0995 s remaining\n", "subpixel-averaging is 10.2289% done, 37.5564 s remaining\n", "subpixel-averaging is 13.6388% done, 27.3374 s remaining\n", "subpixel-averaging is 17.0487% done, 21.2318 s remaining\n", "subpixel-averaging is 20.4586% done, 16.7154 s remaining\n", "subpixel-averaging is 23.8685% done, 13.4232 s remaining\n", "subpixel-averaging is 27.2785% done, 11.4875 s remaining\n", "subpixel-averaging is 30.6884% done, 9.75372 s remaining\n", "subpixel-averaging is 34.0983% done, 8.27854 s remaining\n", "subpixel-averaging is 37.5082% done, 6.93392 s remaining\n", "subpixel-averaging is 65.64% done, 2.22818 s remaining\n", "subpixel-averaging is 69.0499% done, 1.90412 s remaining\n", "subpixel-averaging is 72.4598% done, 1.64002 s remaining\n", "subpixel-averaging is 75.8697% done, 1.32216 s remaining\n", "subpixel-averaging is 79.2797% done, 1.09069 s remaining\n", "subpixel-averaging is 82.6896% done, 0.886299 s remaining\n", "subpixel-averaging is 86.0995% done, 0.703019 s remaining\n", "subpixel-averaging is 89.5094% done, 0.498196 s remaining\n", "subpixel-averaging is 92.9193% done, 0.325508 s remaining\n", "subpixel-averaging is 98.8867% done, 0.0483677 s remaining\n", "subpixel-averaging is 6.81898% done, 59.2108 s remaining\n", "subpixel-averaging is 10.2289% done, 37.546 s remaining\n", "subpixel-averaging is 13.6388% done, 26.994 s remaining\n", "subpixel-averaging is 17.0487% done, 21.1849 s remaining\n", "subpixel-averaging is 20.4586% done, 16.5219 s remaining\n", "subpixel-averaging is 23.8685% done, 13.1793 s remaining\n", "subpixel-averaging is 27.2785% done, 11.4351 s remaining\n", "subpixel-averaging is 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17.0487% done, 21.2724 s remaining\n", "subpixel-averaging is 20.4586% done, 16.6743 s remaining\n", "subpixel-averaging is 23.8685% done, 13.2109 s remaining\n", "subpixel-averaging is 27.2785% done, 11.3869 s remaining\n", "subpixel-averaging is 30.6884% done, 9.75682 s remaining\n", "subpixel-averaging is 34.0983% done, 8.26105 s remaining\n", "subpixel-averaging is 37.5082% done, 7.09754 s remaining\n", "subpixel-averaging is 65.64% done, 2.24059 s remaining\n", "subpixel-averaging is 69.0499% done, 1.91605 s remaining\n", "subpixel-averaging is 72.4598% done, 1.63231 s remaining\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "subpixel-averaging is 75.8697% done, 1.34937 s remaining\n", "subpixel-averaging is 79.2797% done, 1.08291 s remaining\n", "subpixel-averaging is 82.6896% done, 0.888016 s remaining\n", "subpixel-averaging is 86.0995% done, 0.710327 s remaining\n", "subpixel-averaging is 89.5094% done, 0.493508 s remaining\n", "subpixel-averaging is 92.9193% done, 0.332601 s remaining\n", "subpixel-averaging is 98.8867% done, 0.0488103 s remaining\n", "time for set_epsilon = 271.702 s\n", "-----------\n", "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 22 iters\n", "MPB solved for frequency_1(2.08443,0,0) = 0.645247 after 8 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1eb3cf6322754e07ba3761f3426e295a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatProgress(value=0.0, description='0% done ', max=177.5)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Meep progress: 44.68/177.5 = 25.2% done in 4.0s, 11.9s to go\n", "on time step 2239 (time=44.78), 0.00178698 s/step\n", "Meep progress: 93.64/177.5 = 52.8% done in 8.0s, 7.2s to go\n", "on time step 4687 (time=93.74), 0.00163464 s/step\n", "Meep progress: 142.64000000000001/177.5 = 80.4% done in 12.0s, 2.9s to go\n", "on time step 7139 (time=142.78), 0.00163171 s/step\n", "run 0 finished at t = 177.5 (8875 timesteps)\n" ] } ], "source": [ "import meep as mp\n", "import numpy\n", "import matplotlib.pyplot as plt\n", "\n", "res = 25 # pixels/μm\n", "three_d = False # 3d calculation?\n", "d = 0.12 # branch separation\n", "\n", "gdsII_file = \"coupler.gds\"\n", "CELL_LAYER = 0\n", "PORT1_LAYER = 1\n", "PORT2_LAYER = 2\n", "PORT3_LAYER = 3\n", "PORT4_LAYER = 4\n", "SOURCE_LAYER = 5\n", "UPPER_BRANCH_LAYER = 31\n", "LOWER_BRANCH_LAYER = 32\n", "default_d = 0.3\n", "\n", "t_oxide = 1.0\n", "t_Si = 0.22\n", "t_air = 0.78\n", "\n", "dpml = 1\n", "cell_thickness = dpml + t_oxide + t_Si + t_air + dpml\n", "\n", "oxide = mp.Medium(epsilon=2.25)\n", "silicon = mp.Medium(epsilon=12)\n", "\n", "lcen = 1.55\n", "fcen = 1 / lcen\n", "df = 0.2 * fcen\n", "\n", "cell_zmax = 0.5 * cell_thickness if three_d else 0\n", "cell_zmin = -0.5 * cell_thickness if three_d else 0\n", "si_zmax = 0.5 * t_Si if three_d else 10\n", "si_zmin = -0.5 * t_Si if three_d else -10\n", "\n", "# read cell size, volumes for source region and flux monitors,\n", "# and coupler geometry from GDSII file\n", "upper_branch = mp.get_GDSII_prisms(\n", " silicon, gdsII_file, UPPER_BRANCH_LAYER, si_zmin, si_zmax\n", ")\n", "lower_branch = mp.get_GDSII_prisms(\n", " silicon, gdsII_file, LOWER_BRANCH_LAYER, si_zmin, si_zmax\n", ")\n", "\n", "cell = mp.GDSII_vol(gdsII_file, CELL_LAYER, cell_zmin, cell_zmax)\n", "p1 = mp.GDSII_vol(gdsII_file, PORT1_LAYER, si_zmin, si_zmax)\n", "p2 = mp.GDSII_vol(gdsII_file, PORT2_LAYER, si_zmin, si_zmax)\n", "p3 = mp.GDSII_vol(gdsII_file, PORT3_LAYER, si_zmin, si_zmax)\n", "p4 = mp.GDSII_vol(gdsII_file, PORT4_LAYER, si_zmin, si_zmax)\n", "src_vol = mp.GDSII_vol(gdsII_file, SOURCE_LAYER, si_zmin, si_zmax)\n", "\n", "# displace upper and lower branches of coupler (as well as source and flux regions)\n", "if d != default_d:\n", " delta_y = 0.5 * (d - default_d)\n", " delta = mp.Vector3(y=delta_y)\n", " p1.center += delta\n", " p2.center -= delta\n", " p3.center += delta\n", " p4.center -= delta\n", " src_vol.center += delta\n", " cell.size += 2 * delta\n", " for np in range(len(lower_branch)):\n", " lower_branch[np].center -= delta\n", " for nv in range(len(lower_branch[np].vertices)):\n", " lower_branch[np].vertices[nv] -= delta\n", " for np in range(len(upper_branch)):\n", " upper_branch[np].center += delta\n", " for nv in range(len(upper_branch[np].vertices)):\n", " upper_branch[np].vertices[nv] += delta\n", "\n", "geometry = upper_branch + lower_branch\n", "\n", "if three_d:\n", " oxide_center = mp.Vector3(z=-0.5 * t_oxide)\n", " oxide_size = mp.Vector3(cell.size.x, cell.size.y, t_oxide)\n", " oxide_layer = [mp.Block(material=oxide, center=oxide_center, size=oxide_size)]\n", " geometry = geometry + oxide_layer\n", "\n", "sources = [\n", " mp.EigenModeSource(\n", " src=mp.GaussianSource(fcen, fwidth=df),\n", " size=src_vol.size,\n", " center=src_vol.center,\n", " eig_band=1,\n", " eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z,\n", " eig_match_freq=True,\n", " )\n", "]\n", "\n", "sim = mp.Simulation(\n", " resolution=res,\n", " cell_size=cell.size,\n", " boundary_layers=[mp.PML(dpml)],\n", " sources=sources,\n", " geometry=geometry,\n", ")\n", "\n", "mode1 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p1))\n", "mode2 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p2))\n", "mode3 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p3))\n", "mode4 = sim.add_mode_monitor(fcen, 0, 1, mp.ModeRegion(volume=p4))\n", "\n", "sim.run(until_after_sources=100)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 22 iters\n", "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 5 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.084124,-0.000000,0.000000)\n", "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 12 iters\n", "MPB solved for frequency_1(2.08452,0,0) = 0.645271 after 5 iters\n", "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 3 iters\n", "MPB solved for frequency_1(2.08413,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.084127,-0.000000,0.000000)\n", "MPB solved for frequency_1(2.2349,0,0) = 0.687236 after 8 iters\n", "MPB solved for frequency_1(2.08441,0,0) = 0.645241 after 8 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.084118,-0.000000,0.000000)\n", "MPB solved for frequency_1(2.2349,0,0) = 0.687238 after 27 iters\n", "MPB solved for frequency_1(2.08443,0,0) = 0.645249 after 7 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 3 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n", "Dominant planewave for band 1: (2.084119,-0.000000,0.000000)\n", "trans:, 0.12, 0.000001, 0.406761, 0.587083\n" ] } ], "source": [ "# S parameters\n", "p1_coeff = sim.get_eigenmode_coefficients(\n", " mode1, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", ").alpha[0, 0, 0]\n", "p2_coeff = sim.get_eigenmode_coefficients(\n", " mode2, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", ").alpha[0, 0, 1]\n", "p3_coeff = sim.get_eigenmode_coefficients(\n", " mode3, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", ").alpha[0, 0, 0]\n", "p4_coeff = sim.get_eigenmode_coefficients(\n", " mode4, [1], eig_parity=mp.NO_PARITY if three_d else mp.EVEN_Y + mp.ODD_Z\n", ").alpha[0, 0, 0]\n", "\n", "# transmittance\n", "p2_trans = abs(p2_coeff) ** 2 / abs(p1_coeff) ** 2\n", "p3_trans = abs(p3_coeff) ** 2 / abs(p1_coeff) ** 2\n", "p4_trans = abs(p4_coeff) ** 2 / abs(p1_coeff) ** 2\n", "\n", "print(\"trans:, {:.2f}, {:.6f}, {:.6f}, {:.6f}\".format(d, p2_trans, p3_trans, p4_trans))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a given waveguide separation distance (`d`), the simulation computes the transmittance of Ports 2, 3, and 4. The transmittance is the square of the [S-parameter](https://en.wikipedia.org/wiki/Scattering_parameters) which is equivalent to the [mode coefficient](https://meep.readthedocs.io/en/latest/Mode_Decomposition). There is an additional mode monitor at Port 1 to compute the input power from the adjacent eigenmode source; this is used for normalization when computing the transmittance. The eight layers of the GDSII file are each converted to a `Simulation` object: the upper and lower branches of the coupler are defined as a collection of [`Prism`](https://meep.readthedocs.io/en/latest/Python_User_Interface/#prism)s, the rectilinear regions of the source and flux monitor as a [`Volume`](https://meep.readthedocs.io/en/latest/Python_User_Interface/#volume) and [`FluxRegion`](https://meep.readthedocs.io/en/latest/Python_User_Interface/#fluxregion). The size of the cell in the $y$ direction is dependent on `d`. The default dimensionality is 2d. (Note that for a 2d cell the `Prism` objects returned by `get_GDSII_prisms` must have a finite height. The finite height of `Volume` objects returned by `GDSII_vol` are ignored in 2d.) An optional input parameter (`three_d`) converts the geometry to 3d by extruding the coupler geometry in the *z* direction and adding an oxide layer beneath similar to a [silicon on insulator](https://en.wikipedia.org/wiki/Silicon_on_insulator) (SOI) substrate. A schematic of the coupler design in 3d generated using MayaVi is shown below.\n", "\n", "![](https://meep.readthedocs.io/en/latest/images/coupler3D.png)\n", "\n", "\n", "## Transmittance Results and Field Profiles\n", "\n", "The transmittance results are plotted in the figure below for a range of separation distances from 0.02 to 0.30 μm with increments of 0.02 μm. When the two waveguide branches are sufficiently separated (`d` > 0.2 μm), practically all of the input power remains in the top branch and is transferred to Port 3. A small amount of the input power is lost due to scattering into radiative modes within the light cone in the tapered sections where the translational symmetry of the waveguide is broken. This is why the power in Port 3 never reaches exactly 100%. For separation distances of less than approximately 0.2 μm, evanescent coupling of the modes from the top to the lower branch begins to transfer some of the input power to Port 4. For `d` of 0.13 μm, the input signal is split evenly into Ports 3 and 4. For `d` of 0.06 μm, the input power is transferred completely to Port 4. Finally, for `d` of less than 0.06 μm, the evanescent coupling becomes rapidly ineffective and the signal again remains mostly in Port 3. Note that there is never any power in Port 2 given its location relative to the input from Port 1.\n", "\n", "![](https://meep.readthedocs.io/en/latest/images/directional_coupler_flux.png)\n", "\n", "These quantitative results can also be verified qualitatively using the field profiles shown below for `d` of 0.06, 0.13, and 0.30 μm. To generate these images, the pulse source is replaced with a [continuous wave](https://meep.readthedocs.io/en/latest/Python_User_Interface/#continuoussource) (CW) and the fields are time stepped for a sufficiently long run time until they have reached steady state. The [array slicing](https://meep.readthedocs.io/en/latest/Python_User_Interface/#array-slices) routines `get_epsilon` and `get_efield_z` are then used to obtain the dielectric and field data over the entire cell." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------\n", "Initializing structure...\n", "time for choose_chunkdivision = 0.000232935 s\n", "Working in 2D dimensions.\n", "Computational cell is 34.4 x 7.84 x 0 with resolution 25\n", " prism, center = (-9.09425,1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (-4,0.06,-10)\n", " (-4.108,0.061,-10)\n", " (-4.215,0.062,-10)\n", " (-4.322,0.065,-10)\n", " (-4.429,0.07,-10)\n", " (-4.535,0.075,-10)\n", " (-4.641,0.081,-10)\n", " (-4.747,0.089,-10)\n", " (-4.852,0.097,-10)\n", " (-5.062,0.117,-10)\n", " (-5.167,0.129,-10)\n", " (-5.271,0.141,-10)\n", " (-5.479,0.169,-10)\n", " (-5.582,0.184,-10)\n", " (-5.685,0.2,-10)\n", " (-5.788,0.217,-10)\n", " (-5.891,0.235,-10)\n", " (-5.993,0.253,-10)\n", " (-6.095,0.272,-10)\n", " (-6.197,0.292,-10)\n", " (-6.299,0.313,-10)\n", " (-6.4,0.334,-10)\n", " (-6.501,0.356,-10)\n", " (-6.703,0.402,-10)\n", " (-6.803,0.425,-10)\n", " (-6.904,0.45,-10)\n", " (-7.204,0.525,-10)\n", " (-7.303,0.552,-10)\n", " (-7.403,0.578,-10)\n", " (-7.502,0.605,-10)\n", " (-7.601,0.633,-10)\n", " (-7.7,0.66,-10)\n", " (-7.799,0.688,-10)\n", " (-7.898,0.717,-10)\n", " (-7.996,0.745,-10)\n", " (-8.095,0.774,-10)\n", " (-8.193,0.803,-10)\n", " (-8.291,0.833,-10)\n", " (-8.389,0.862,-10)\n", " (-8.487,0.892,-10)\n", " (-8.585,0.921,-10)\n", " (-8.781,0.981,-10)\n", " (-8.878,1.011,-10)\n", " (-9.074,1.071,-10)\n", " (-9.268,1.131,-10)\n", " (-9.366,1.161,-10)\n", " (-9.463,1.191,-10)\n", " (-9.56,1.22,-10)\n", " (-9.658,1.25,-10)\n", " (-9.949,1.337,-10)\n", " (-10.047,1.366,-10)\n", " (-10.144,1.395,-10)\n", " (-10.338,1.451,-10)\n", " (-10.436,1.478,-10)\n", " (-10.533,1.506,-10)\n", " (-10.63,1.533,-10)\n", " (-10.728,1.559,-10)\n", " (-10.922,1.611,-10)\n", " (-11.02,1.636,-10)\n", " (-11.117,1.66,-10)\n", " (-11.215,1.685,-10)\n", " (-11.313,1.708,-10)\n", " (-11.41,1.731,-10)\n", " (-11.508,1.754,-10)\n", " (-11.606,1.776,-10)\n", " (-11.704,1.797,-10)\n", " (-11.802,1.817,-10)\n", " (-11.901,1.837,-10)\n", " (-11.999,1.856,-10)\n", " (-12.098,1.875,-10)\n", " (-12.196,1.893,-10)\n", " (-12.295,1.91,-10)\n", " (-12.394,1.926,-10)\n", " (-12.592,1.956,-10)\n", " (-12.691,1.97,-10)\n", " (-12.791,1.982,-10)\n", " (-12.89,1.994,-10)\n", " (-12.99,2.005,-10)\n", " (-13.19,2.025,-10)\n", " (-13.291,2.033,-10)\n", " (-13.392,2.04,-10)\n", " (-13.492,2.046,-10)\n", " (-13.593,2.051,-10)\n", " (-13.695,2.055,-10)\n", " (-13.796,2.058,-10)\n", " (-14,2.06,-10)\n", " (-17.2,2.06,-10)\n", " (-17.2,2.56,-10)\n", " (-14,2.56,-10)\n", " (-13.892,2.559,-10)\n", " (-13.785,2.558,-10)\n", " (-13.678,2.555,-10)\n", " (-13.571,2.55,-10)\n", " (-13.465,2.545,-10)\n", " (-13.359,2.539,-10)\n", " (-13.253,2.531,-10)\n", " (-13.148,2.523,-10)\n", " (-12.938,2.503,-10)\n", " (-12.833,2.491,-10)\n", " (-12.729,2.479,-10)\n", " (-12.521,2.451,-10)\n", " (-12.418,2.436,-10)\n", " (-12.315,2.42,-10)\n", " (-12.212,2.403,-10)\n", " (-12.109,2.385,-10)\n", " (-12.007,2.367,-10)\n", " (-11.905,2.348,-10)\n", " (-11.803,2.328,-10)\n", " (-11.701,2.307,-10)\n", " (-11.6,2.286,-10)\n", " (-11.499,2.264,-10)\n", " (-11.297,2.218,-10)\n", " (-11.197,2.195,-10)\n", " (-11.096,2.17,-10)\n", " (-10.796,2.095,-10)\n", " (-10.697,2.068,-10)\n", " (-10.597,2.042,-10)\n", " (-10.498,2.015,-10)\n", " (-10.399,1.987,-10)\n", " (-10.3,1.96,-10)\n", " (-10.201,1.932,-10)\n", " (-10.102,1.903,-10)\n", " (-10.004,1.875,-10)\n", " (-9.905,1.846,-10)\n", " (-9.807,1.817,-10)\n", " (-9.709,1.787,-10)\n", " (-9.611,1.758,-10)\n", " (-9.513,1.728,-10)\n", " (-9.415,1.699,-10)\n", " (-9.219,1.639,-10)\n", " (-9.122,1.609,-10)\n", " (-8.926,1.549,-10)\n", " (-8.732,1.489,-10)\n", " (-8.634,1.459,-10)\n", " (-8.537,1.429,-10)\n", " (-8.44,1.4,-10)\n", " (-8.342,1.37,-10)\n", " (-8.051,1.283,-10)\n", " (-7.953,1.254,-10)\n", " (-7.856,1.225,-10)\n", " (-7.662,1.169,-10)\n", " (-7.564,1.142,-10)\n", " (-7.467,1.114,-10)\n", " (-7.37,1.087,-10)\n", " (-7.272,1.061,-10)\n", " (-7.078,1.009,-10)\n", " (-6.98,0.984,-10)\n", " (-6.883,0.96,-10)\n", " (-6.785,0.935,-10)\n", " (-6.687,0.912,-10)\n", " (-6.59,0.889,-10)\n", " (-6.492,0.866,-10)\n", " (-6.394,0.844,-10)\n", " (-6.296,0.823,-10)\n", " (-6.198,0.803,-10)\n", " (-6.099,0.783,-10)\n", " (-6.001,0.764,-10)\n", " (-5.902,0.745,-10)\n", " (-5.804,0.727,-10)\n", " (-5.705,0.71,-10)\n", " (-5.606,0.694,-10)\n", " (-5.408,0.664,-10)\n", " (-5.309,0.65,-10)\n", " (-5.209,0.638,-10)\n", " (-5.11,0.626,-10)\n", " (-5.01,0.615,-10)\n", " (-4.81,0.595,-10)\n", " (-4.709,0.587,-10)\n", " (-4.608,0.58,-10)\n", " (-4.508,0.574,-10)\n", " (-4.407,0.569,-10)\n", " (-4.305,0.565,-10)\n", " (-4.204,0.562,-10)\n", " (-4,0.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (4,0.06,-10)\n", " (4,0.56,-10)\n", " (4.204,0.562,-10)\n", " (4.305,0.565,-10)\n", " (4.407,0.569,-10)\n", " (4.508,0.574,-10)\n", " (4.608,0.58,-10)\n", " (4.709,0.587,-10)\n", " (4.81,0.595,-10)\n", " (5.01,0.615,-10)\n", " (5.11,0.626,-10)\n", " (5.209,0.638,-10)\n", " (5.309,0.65,-10)\n", " (5.408,0.664,-10)\n", " (5.606,0.694,-10)\n", " (5.705,0.71,-10)\n", " (5.804,0.727,-10)\n", " (5.902,0.745,-10)\n", " (6.001,0.764,-10)\n", " (6.099,0.783,-10)\n", " (6.198,0.803,-10)\n", " (6.296,0.823,-10)\n", " (6.394,0.844,-10)\n", " (6.492,0.866,-10)\n", " (6.59,0.889,-10)\n", " (6.687,0.912,-10)\n", " (6.785,0.935,-10)\n", " (6.883,0.96,-10)\n", " (6.98,0.984,-10)\n", " (7.078,1.009,-10)\n", " (7.272,1.061,-10)\n", " (7.37,1.087,-10)\n", " (7.467,1.114,-10)\n", " (7.564,1.142,-10)\n", " (7.662,1.169,-10)\n", " (7.856,1.225,-10)\n", " (7.953,1.254,-10)\n", " (8.051,1.283,-10)\n", " (8.342,1.37,-10)\n", " (8.44,1.4,-10)\n", " (8.537,1.429,-10)\n", " (8.634,1.459,-10)\n", " (8.732,1.489,-10)\n", " (8.926,1.549,-10)\n", " (9.122,1.609,-10)\n", " (9.219,1.639,-10)\n", " (9.415,1.699,-10)\n", " (9.513,1.728,-10)\n", " (9.611,1.758,-10)\n", " (9.709,1.787,-10)\n", " (9.807,1.817,-10)\n", " (9.905,1.846,-10)\n", " (10.004,1.875,-10)\n", " (10.102,1.903,-10)\n", " (10.201,1.932,-10)\n", " (10.3,1.96,-10)\n", " (10.399,1.987,-10)\n", " (10.498,2.015,-10)\n", " (10.597,2.042,-10)\n", " (10.697,2.068,-10)\n", " (10.796,2.095,-10)\n", " (11.096,2.17,-10)\n", " (11.197,2.195,-10)\n", " (11.297,2.218,-10)\n", " (11.499,2.264,-10)\n", " (11.6,2.286,-10)\n", " (11.701,2.307,-10)\n", " (11.803,2.328,-10)\n", " (11.905,2.348,-10)\n", " (12.007,2.367,-10)\n", " (12.109,2.385,-10)\n", " (12.212,2.403,-10)\n", " (12.315,2.42,-10)\n", " (12.418,2.436,-10)\n", " (12.521,2.451,-10)\n", " (12.729,2.479,-10)\n", " (12.833,2.491,-10)\n", " (12.938,2.503,-10)\n", " (13.148,2.523,-10)\n", " (13.253,2.531,-10)\n", " (13.359,2.539,-10)\n", " (13.465,2.545,-10)\n", " (13.571,2.55,-10)\n", " (13.678,2.555,-10)\n", " (13.785,2.558,-10)\n", " (13.892,2.559,-10)\n", " (14,2.56,-10)\n", " (17.2,2.56,-10)\n", " (17.2,2.06,-10)\n", " (14,2.06,-10)\n", " (13.796,2.058,-10)\n", " (13.695,2.055,-10)\n", " (13.593,2.051,-10)\n", " (13.492,2.046,-10)\n", " (13.392,2.04,-10)\n", " (13.291,2.033,-10)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (13.19,2.025,-10)\n", " (12.99,2.005,-10)\n", " (12.89,1.994,-10)\n", " (12.791,1.982,-10)\n", " (12.691,1.97,-10)\n", " (12.592,1.956,-10)\n", " (12.394,1.926,-10)\n", " (12.295,1.91,-10)\n", " (12.196,1.893,-10)\n", " (12.098,1.875,-10)\n", " (11.999,1.856,-10)\n", " (11.901,1.837,-10)\n", " (11.802,1.817,-10)\n", " (11.704,1.797,-10)\n", " (11.606,1.776,-10)\n", " (11.508,1.754,-10)\n", " (11.41,1.731,-10)\n", " (11.313,1.708,-10)\n", " (11.215,1.685,-10)\n", " (11.117,1.66,-10)\n", " (11.02,1.636,-10)\n", " (10.922,1.611,-10)\n", " (10.728,1.559,-10)\n", " (10.63,1.533,-10)\n", " (10.533,1.506,-10)\n", " (10.436,1.478,-10)\n", " (10.338,1.451,-10)\n", " (10.144,1.395,-10)\n", " (10.047,1.366,-10)\n", " (9.949,1.337,-10)\n", " (9.658,1.25,-10)\n", " (9.56,1.22,-10)\n", " (9.463,1.191,-10)\n", " (9.366,1.161,-10)\n", " (9.268,1.131,-10)\n", " (9.074,1.071,-10)\n", " (8.878,1.011,-10)\n", " (8.781,0.981,-10)\n", " (8.585,0.921,-10)\n", " (8.487,0.892,-10)\n", " (8.389,0.862,-10)\n", " (8.291,0.833,-10)\n", " (8.193,0.803,-10)\n", " (8.095,0.774,-10)\n", " (7.996,0.745,-10)\n", " (7.898,0.717,-10)\n", " (7.799,0.688,-10)\n", " (7.7,0.66,-10)\n", " (7.601,0.633,-10)\n", " (7.502,0.605,-10)\n", " (7.403,0.578,-10)\n", " (7.303,0.552,-10)\n", " (7.204,0.525,-10)\n", " (6.904,0.45,-10)\n", " (6.803,0.425,-10)\n", " (6.703,0.402,-10)\n", " (6.501,0.356,-10)\n", " (6.4,0.334,-10)\n", " (6.299,0.313,-10)\n", " (6.197,0.292,-10)\n", " (6.095,0.272,-10)\n", " (5.993,0.253,-10)\n", " (5.891,0.235,-10)\n", " (5.788,0.217,-10)\n", " (5.685,0.2,-10)\n", " (5.582,0.184,-10)\n", " (5.479,0.169,-10)\n", " (5.271,0.141,-10)\n", " (5.167,0.129,-10)\n", " (5.062,0.117,-10)\n", " (4.852,0.097,-10)\n", " (4.747,0.089,-10)\n", " (4.641,0.081,-10)\n", " (4.535,0.075,-10)\n", " (4.429,0.07,-10)\n", " (4.322,0.065,-10)\n", " (4.215,0.062,-10)\n", " (4.108,0.061,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,0.31,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", " (-4,0.06,-10)\n", " (-4,0.56,-10)\n", " (4,0.56,-10)\n", " (4,0.06,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (-9.09425,-1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (-17.2,-2.56,-10)\n", " (-17.2,-2.06,-10)\n", " (-14,-2.06,-10)\n", " (-13.796,-2.058,-10)\n", " (-13.695,-2.055,-10)\n", " (-13.593,-2.051,-10)\n", " (-13.492,-2.046,-10)\n", " (-13.392,-2.04,-10)\n", " (-13.291,-2.033,-10)\n", " (-13.19,-2.025,-10)\n", " (-12.99,-2.005,-10)\n", " (-12.89,-1.994,-10)\n", " (-12.791,-1.982,-10)\n", " (-12.691,-1.97,-10)\n", " (-12.592,-1.956,-10)\n", " (-12.394,-1.926,-10)\n", " (-12.295,-1.91,-10)\n", " (-12.196,-1.893,-10)\n", " (-12.098,-1.875,-10)\n", " (-11.999,-1.856,-10)\n", " (-11.901,-1.837,-10)\n", " (-11.802,-1.817,-10)\n", " (-11.704,-1.797,-10)\n", " (-11.606,-1.776,-10)\n", " (-11.508,-1.754,-10)\n", " (-11.41,-1.731,-10)\n", " (-11.313,-1.708,-10)\n", " (-11.215,-1.685,-10)\n", " (-11.117,-1.66,-10)\n", " (-11.02,-1.636,-10)\n", " (-10.922,-1.611,-10)\n", " (-10.728,-1.559,-10)\n", " (-10.63,-1.533,-10)\n", " (-10.533,-1.506,-10)\n", " (-10.436,-1.478,-10)\n", " (-10.338,-1.451,-10)\n", " (-10.144,-1.395,-10)\n", " (-10.047,-1.366,-10)\n", " (-9.949,-1.337,-10)\n", " (-9.658,-1.25,-10)\n", " (-9.56,-1.22,-10)\n", " (-9.463,-1.191,-10)\n", " (-9.366,-1.161,-10)\n", " (-9.268,-1.131,-10)\n", " (-9.074,-1.071,-10)\n", " (-8.878,-1.011,-10)\n", " (-8.781,-0.981,-10)\n", " (-8.585,-0.921,-10)\n", " (-8.487,-0.892,-10)\n", " (-8.389,-0.862,-10)\n", " (-8.291,-0.833,-10)\n", " (-8.193,-0.803,-10)\n", " (-8.095,-0.774,-10)\n", " (-7.996,-0.745,-10)\n", " (-7.898,-0.717,-10)\n", " (-7.799,-0.688,-10)\n", " (-7.7,-0.66,-10)\n", " (-7.601,-0.633,-10)\n", " (-7.502,-0.605,-10)\n", " (-7.403,-0.578,-10)\n", " (-7.303,-0.552,-10)\n", " (-7.204,-0.525,-10)\n", " (-6.904,-0.45,-10)\n", " (-6.803,-0.425,-10)\n", " (-6.703,-0.402,-10)\n", " (-6.501,-0.356,-10)\n", " (-6.4,-0.334,-10)\n", " (-6.299,-0.313,-10)\n", " (-6.197,-0.292,-10)\n", " (-6.095,-0.272,-10)\n", " (-5.993,-0.253,-10)\n", " (-5.891,-0.235,-10)\n", " (-5.788,-0.217,-10)\n", " (-5.685,-0.2,-10)\n", " (-5.582,-0.184,-10)\n", " (-5.479,-0.169,-10)\n", " (-5.271,-0.141,-10)\n", " (-5.167,-0.129,-10)\n", " (-5.062,-0.117,-10)\n", " (-4.852,-0.097,-10)\n", " (-4.747,-0.089,-10)\n", " (-4.641,-0.081,-10)\n", " (-4.535,-0.075,-10)\n", " (-4.429,-0.07,-10)\n", " (-4.322,-0.065,-10)\n", " (-4.215,-0.062,-10)\n", " (-4.108,-0.061,-10)\n", " (-4,-0.06,-10)\n", " (-4,-0.56,-10)\n", " (-4.204,-0.562,-10)\n", " (-4.305,-0.565,-10)\n", " (-4.407,-0.569,-10)\n", " (-4.508,-0.574,-10)\n", " (-4.608,-0.58,-10)\n", " (-4.709,-0.587,-10)\n", " (-4.81,-0.595,-10)\n", " (-5.01,-0.615,-10)\n", " (-5.11,-0.626,-10)\n", " (-5.209,-0.638,-10)\n", " (-5.309,-0.65,-10)\n", " (-5.408,-0.664,-10)\n", " (-5.606,-0.694,-10)\n", " (-5.705,-0.71,-10)\n", " (-5.804,-0.727,-10)\n", " (-5.902,-0.745,-10)\n", " (-6.001,-0.764,-10)\n", " (-6.099,-0.783,-10)\n", " (-6.198,-0.803,-10)\n", " (-6.296,-0.823,-10)\n", " (-6.394,-0.844,-10)\n", " (-6.492,-0.866,-10)\n", " (-6.59,-0.889,-10)\n", " (-6.687,-0.912,-10)\n", " (-6.785,-0.935,-10)\n", " (-6.883,-0.96,-10)\n", " (-6.98,-0.984,-10)\n", " (-7.078,-1.009,-10)\n", " (-7.272,-1.061,-10)\n", " (-7.37,-1.087,-10)\n", " (-7.467,-1.114,-10)\n", " (-7.564,-1.142,-10)\n", " (-7.662,-1.169,-10)\n", " (-7.856,-1.225,-10)\n", " (-7.953,-1.254,-10)\n", " (-8.051,-1.283,-10)\n", " (-8.342,-1.37,-10)\n", " (-8.44,-1.4,-10)\n", " (-8.537,-1.429,-10)\n", " (-8.634,-1.459,-10)\n", " (-8.732,-1.489,-10)\n", " (-8.926,-1.549,-10)\n", " (-9.122,-1.609,-10)\n", " (-9.219,-1.639,-10)\n", " (-9.415,-1.699,-10)\n", " (-9.513,-1.728,-10)\n", " (-9.611,-1.758,-10)\n", " (-9.709,-1.787,-10)\n", " (-9.807,-1.817,-10)\n", " (-9.905,-1.846,-10)\n", " (-10.004,-1.875,-10)\n", " (-10.102,-1.903,-10)\n", " (-10.201,-1.932,-10)\n", " (-10.3,-1.96,-10)\n", " (-10.399,-1.987,-10)\n", " (-10.498,-2.015,-10)\n", " (-10.597,-2.042,-10)\n", " (-10.697,-2.068,-10)\n", " (-10.796,-2.095,-10)\n", " (-11.096,-2.17,-10)\n", " (-11.197,-2.195,-10)\n", " (-11.297,-2.218,-10)\n", " (-11.499,-2.264,-10)\n", " (-11.6,-2.286,-10)\n", " (-11.701,-2.307,-10)\n", " (-11.803,-2.328,-10)\n", " (-11.905,-2.348,-10)\n", " (-12.007,-2.367,-10)\n", " (-12.109,-2.385,-10)\n", " (-12.212,-2.403,-10)\n", " (-12.315,-2.42,-10)\n", " (-12.418,-2.436,-10)\n", " (-12.521,-2.451,-10)\n", " (-12.729,-2.479,-10)\n", " (-12.833,-2.491,-10)\n", " (-12.938,-2.503,-10)\n", " (-13.148,-2.523,-10)\n", " (-13.253,-2.531,-10)\n", " (-13.359,-2.539,-10)\n", " (-13.465,-2.545,-10)\n", " (-13.571,-2.55,-10)\n", " (-13.678,-2.555,-10)\n", " (-13.785,-2.558,-10)\n", " (-13.892,-2.559,-10)\n", " (-14,-2.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (9.09425,-1.32149,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 174 vertices:\n", " (14,-2.56,-10)\n", " (13.892,-2.559,-10)\n", " (13.785,-2.558,-10)\n", " (13.678,-2.555,-10)\n", " (13.571,-2.55,-10)\n", " (13.465,-2.545,-10)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " (13.359,-2.539,-10)\n", " (13.253,-2.531,-10)\n", " (13.148,-2.523,-10)\n", " (12.938,-2.503,-10)\n", " (12.833,-2.491,-10)\n", " (12.729,-2.479,-10)\n", " (12.521,-2.451,-10)\n", " (12.418,-2.436,-10)\n", " (12.315,-2.42,-10)\n", " (12.212,-2.403,-10)\n", " (12.109,-2.385,-10)\n", " (12.007,-2.367,-10)\n", " (11.905,-2.348,-10)\n", " (11.803,-2.328,-10)\n", " (11.701,-2.307,-10)\n", " (11.6,-2.286,-10)\n", " (11.499,-2.264,-10)\n", " (11.297,-2.218,-10)\n", " (11.197,-2.195,-10)\n", " (11.096,-2.17,-10)\n", " (10.796,-2.095,-10)\n", " (10.697,-2.068,-10)\n", " (10.597,-2.042,-10)\n", " (10.498,-2.015,-10)\n", " (10.399,-1.987,-10)\n", " (10.3,-1.96,-10)\n", " (10.201,-1.932,-10)\n", " (10.102,-1.903,-10)\n", " (10.004,-1.875,-10)\n", " (9.905,-1.846,-10)\n", " (9.807,-1.817,-10)\n", " (9.709,-1.787,-10)\n", " (9.611,-1.758,-10)\n", " (9.513,-1.728,-10)\n", " (9.415,-1.699,-10)\n", " (9.219,-1.639,-10)\n", " (9.122,-1.609,-10)\n", " (8.926,-1.549,-10)\n", " (8.732,-1.489,-10)\n", " (8.634,-1.459,-10)\n", " (8.537,-1.429,-10)\n", " (8.44,-1.4,-10)\n", " (8.342,-1.37,-10)\n", " (8.051,-1.283,-10)\n", " (7.953,-1.254,-10)\n", " (7.856,-1.225,-10)\n", " (7.662,-1.169,-10)\n", " (7.564,-1.142,-10)\n", " (7.467,-1.114,-10)\n", " (7.37,-1.087,-10)\n", " (7.272,-1.061,-10)\n", " (7.078,-1.009,-10)\n", " (6.98,-0.984,-10)\n", " (6.883,-0.96,-10)\n", " (6.785,-0.935,-10)\n", " (6.687,-0.912,-10)\n", " (6.59,-0.889,-10)\n", " (6.492,-0.866,-10)\n", " (6.394,-0.844,-10)\n", " (6.296,-0.823,-10)\n", " (6.198,-0.803,-10)\n", " (6.099,-0.783,-10)\n", " (6.001,-0.764,-10)\n", " (5.902,-0.745,-10)\n", " (5.804,-0.727,-10)\n", " (5.705,-0.71,-10)\n", " (5.606,-0.694,-10)\n", " (5.408,-0.664,-10)\n", " (5.309,-0.65,-10)\n", " (5.209,-0.638,-10)\n", " (5.11,-0.626,-10)\n", " (5.01,-0.615,-10)\n", " (4.81,-0.595,-10)\n", " (4.709,-0.587,-10)\n", " (4.608,-0.58,-10)\n", " (4.508,-0.574,-10)\n", " (4.407,-0.569,-10)\n", " (4.305,-0.565,-10)\n", " (4.204,-0.562,-10)\n", " (4,-0.56,-10)\n", " (4,-0.06,-10)\n", " (4.108,-0.061,-10)\n", " (4.215,-0.062,-10)\n", " (4.322,-0.065,-10)\n", " (4.429,-0.07,-10)\n", " (4.535,-0.075,-10)\n", " (4.641,-0.081,-10)\n", " (4.747,-0.089,-10)\n", " (4.852,-0.097,-10)\n", " (5.062,-0.117,-10)\n", " (5.167,-0.129,-10)\n", " (5.271,-0.141,-10)\n", " (5.479,-0.169,-10)\n", " (5.582,-0.184,-10)\n", " (5.685,-0.2,-10)\n", " (5.788,-0.217,-10)\n", " (5.891,-0.235,-10)\n", " (5.993,-0.253,-10)\n", " (6.095,-0.272,-10)\n", " (6.197,-0.292,-10)\n", " (6.299,-0.313,-10)\n", " (6.4,-0.334,-10)\n", " (6.501,-0.356,-10)\n", " (6.703,-0.402,-10)\n", " (6.803,-0.425,-10)\n", " (6.904,-0.45,-10)\n", " (7.204,-0.525,-10)\n", " (7.303,-0.552,-10)\n", " (7.403,-0.578,-10)\n", " (7.502,-0.605,-10)\n", " (7.601,-0.633,-10)\n", " (7.7,-0.66,-10)\n", " (7.799,-0.688,-10)\n", " (7.898,-0.717,-10)\n", " (7.996,-0.745,-10)\n", " (8.095,-0.774,-10)\n", " (8.193,-0.803,-10)\n", " (8.291,-0.833,-10)\n", " (8.389,-0.862,-10)\n", " (8.487,-0.892,-10)\n", " (8.585,-0.921,-10)\n", " (8.781,-0.981,-10)\n", " (8.878,-1.011,-10)\n", " (9.074,-1.071,-10)\n", " (9.268,-1.131,-10)\n", " (9.366,-1.161,-10)\n", " (9.463,-1.191,-10)\n", " (9.56,-1.22,-10)\n", " (9.658,-1.25,-10)\n", " (9.949,-1.337,-10)\n", " (10.047,-1.366,-10)\n", " (10.144,-1.395,-10)\n", " (10.338,-1.451,-10)\n", " (10.436,-1.478,-10)\n", " (10.533,-1.506,-10)\n", " (10.63,-1.533,-10)\n", " (10.728,-1.559,-10)\n", " (10.922,-1.611,-10)\n", " (11.02,-1.636,-10)\n", " (11.117,-1.66,-10)\n", " (11.215,-1.685,-10)\n", " (11.313,-1.708,-10)\n", " (11.41,-1.731,-10)\n", " (11.508,-1.754,-10)\n", " (11.606,-1.776,-10)\n", " (11.704,-1.797,-10)\n", " (11.802,-1.817,-10)\n", " (11.901,-1.837,-10)\n", " (11.999,-1.856,-10)\n", " (12.098,-1.875,-10)\n", " (12.196,-1.893,-10)\n", " (12.295,-1.91,-10)\n", " (12.394,-1.926,-10)\n", " (12.592,-1.956,-10)\n", " (12.691,-1.97,-10)\n", " (12.791,-1.982,-10)\n", " (12.89,-1.994,-10)\n", " (12.99,-2.005,-10)\n", " (13.19,-2.025,-10)\n", " (13.291,-2.033,-10)\n", " (13.392,-2.04,-10)\n", " (13.492,-2.046,-10)\n", " (13.593,-2.051,-10)\n", " (13.695,-2.055,-10)\n", " (13.796,-2.058,-10)\n", " (14,-2.06,-10)\n", " (17.2,-2.06,-10)\n", " (17.2,-2.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", " prism, center = (0,-0.31,0)\n", " height 20, axis (0,0,1), sidewall angle: 0 radians, 4 vertices:\n", " (-4,-0.56,-10)\n", " (-4,-0.06,-10)\n", " (4,-0.06,-10)\n", " (4,-0.56,-10)\n", " dielectric constant epsilon diagonal = (12,12,12)\n", "subpixel-averaging is 6.81898% done, 58.5777 s remaining\n", "subpixel-averaging is 10.2289% done, 37.5198 s remaining\n", "subpixel-averaging is 13.6388% done, 27.1639 s remaining\n", "subpixel-averaging is 17.0487% done, 21.1306 s remaining\n", "subpixel-averaging is 20.4586% done, 16.5868 s remaining\n", "subpixel-averaging is 23.8685% done, 13.3518 s remaining\n", "subpixel-averaging is 27.2785% done, 11.5065 s remaining\n", "subpixel-averaging is 30.6884% done, 9.72629 s remaining\n", "subpixel-averaging is 34.0983% done, 7.75935 s remaining\n", "subpixel-averaging is 37.5082% done, 6.95236 s remaining\n", "subpixel-averaging is 65.64% done, 2.22683 s remaining\n", "subpixel-averaging is 69.0499% done, 1.91287 s remaining\n", "subpixel-averaging is 72.4598% done, 1.63956 s remaining\n", "subpixel-averaging is 75.8697% done, 1.36389 s remaining\n", "subpixel-averaging is 79.2797% done, 1.08924 s remaining\n", "subpixel-averaging is 82.6896% done, 0.888155 s remaining\n", "subpixel-averaging is 86.0995% done, 0.678355 s remaining\n", "subpixel-averaging is 89.5094% done, 0.499442 s remaining\n", "subpixel-averaging is 92.9193% done, 0.326281 s remaining\n", "subpixel-averaging is 98.8867% done, 0.0483846 s remaining\n", "subpixel-averaging is 6.81898% done, 59.1477 s remaining\n", "subpixel-averaging is 10.2289% done, 37.4525 s remaining\n", "subpixel-averaging is 13.6388% done, 26.9655 s remaining\n", "subpixel-averaging is 17.0487% done, 21.0934 s remaining\n", "subpixel-averaging is 20.4586% done, 16.5002 s remaining\n", "subpixel-averaging is 23.8685% done, 13.1996 s remaining\n", "subpixel-averaging is 27.2785% done, 11.4347 s remaining\n", "subpixel-averaging is 30.6884% done, 9.76244 s remaining\n", "subpixel-averaging is 34.0983% done, 8.23291 s remaining\n", "subpixel-averaging is 37.5082% done, 7.1923 s remaining\n", "subpixel-averaging is 65.64% done, 2.28558 s remaining\n", "subpixel-averaging is 69.0499% done, 1.93198 s remaining\n", "subpixel-averaging is 72.4598% done, 1.63656 s remaining\n", "subpixel-averaging is 75.8697% done, 1.36626 s remaining\n", "subpixel-averaging is 79.2797% done, 1.08377 s remaining\n", "subpixel-averaging is 82.6896% done, 0.8822 s remaining\n", "subpixel-averaging is 86.0995% done, 0.711633 s remaining\n", "subpixel-averaging is 89.5094% done, 0.493514 s remaining\n", "subpixel-averaging is 92.9193% done, 0.328464 s remaining\n", "subpixel-averaging is 98.8867% done, 0.0470705 s remaining\n", "subpixel-averaging is 6.81898% done, 58.5464 s remaining\n", "subpixel-averaging is 10.2289% done, 38.1292 s remaining\n", "subpixel-averaging is 13.6388% done, 26.9734 s remaining\n", "subpixel-averaging is 17.0487% done, 21.2453 s remaining\n", "subpixel-averaging is 20.4586% done, 16.6093 s remaining\n", "subpixel-averaging is 23.8685% done, 13.2456 s remaining\n", "subpixel-averaging is 27.2785% done, 11.379 s remaining\n", "subpixel-averaging is 30.6884% done, 9.74246 s remaining\n", "subpixel-averaging is 34.0983% done, 8.25901 s remaining\n", "subpixel-averaging is 37.5082% done, 7.07729 s remaining\n", "subpixel-averaging is 65.64% done, 2.23432 s remaining\n", "subpixel-averaging is 69.0499% done, 1.91417 s remaining\n", "subpixel-averaging is 72.4598% done, 1.63028 s remaining\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "subpixel-averaging is 75.8697% done, 1.34979 s remaining\n", "subpixel-averaging is 79.2797% done, 1.08361 s remaining\n", "subpixel-averaging is 82.6896% done, 0.888317 s remaining\n", "subpixel-averaging is 86.0995% done, 0.711677 s remaining\n", "subpixel-averaging is 89.5094% done, 0.492528 s remaining\n", "subpixel-averaging is 92.9193% done, 0.332357 s remaining\n", "subpixel-averaging is 98.8867% done, 0.0488405 s remaining\n", "time for set_epsilon = 271.364 s\n", "-----------\n", "MPB solved for frequency_1(2.2349,0,0) = 0.687243 after 9 iters\n", "MPB solved for frequency_1(2.08451,0,0) = 0.64527 after 4 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 4 iters\n", "MPB solved for frequency_1(2.08412,0,0) = 0.645161 after 1 iters\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19ca7d2433564cacb893306433113d88", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatProgress(value=0.0, description='0% done ', max=400.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Meep progress: 44.980000000000004/400.0 = 11.2% done in 4.0s, 31.6s to go\n", "on time step 2255 (time=45.1), 0.00177414 s/step\n", "Meep progress: 94.72/400.0 = 23.7% done in 8.0s, 25.8s to go\n", "on time step 4743 (time=94.86), 0.00160783 s/step\n", "Meep progress: 144.34/400.0 = 36.1% done in 12.0s, 21.3s to go\n", "on time step 7224 (time=144.48), 0.00161273 s/step\n", "Meep progress: 194.36/400.0 = 48.6% done in 16.0s, 16.9s to go\n", "on time step 9725 (time=194.5), 0.00159943 s/step\n", "Meep progress: 243.5/400.0 = 60.9% done in 20.0s, 12.9s to go\n", "on time step 12184 (time=243.68), 0.00162705 s/step\n", "Meep progress: 293.78000000000003/400.0 = 73.4% done in 24.0s, 8.7s to go\n", "on time step 14699 (time=293.98), 0.00159097 s/step\n", "Meep progress: 343.84000000000003/400.0 = 86.0% done in 28.0s, 4.6s to go\n", "on time step 17201 (time=344.02), 0.00159902 s/step\n", "Meep progress: 393.40000000000003/400.0 = 98.4% done in 32.0s, 0.5s to go\n", "on time step 19681 (time=393.62), 0.00161343 s/step\n", "run 0 finished at t = 400.0 (20000 timesteps)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sim.reset_meep()\n", "\n", "sources = [\n", " mp.EigenModeSource(\n", " src=mp.ContinuousSource(fcen, fwidth=df),\n", " size=src_vol.size,\n", " center=src_vol.center,\n", " eig_band=1,\n", " eig_parity=mp.EVEN_Y + mp.ODD_Z,\n", " eig_match_freq=True,\n", " )\n", "]\n", "\n", "sim = mp.Simulation(\n", " resolution=res,\n", " cell_size=cell.size,\n", " boundary_layers=[mp.PML(dpml)],\n", " sources=sources,\n", " geometry=geometry,\n", ")\n", "\n", "sim.run(\n", " until=400\n", ") # arbitrary long run time to ensure that fields have reached steady state\n", "\n", "eps_data = sim.get_epsilon()\n", "ez_data = numpy.real(sim.get_efield_z())\n", "\n", "plt.figure(dpi=200)\n", "plt.imshow(numpy.transpose(eps_data), interpolation=\"spline36\", cmap=\"binary\")\n", "plt.imshow(\n", " numpy.flipud(numpy.transpose(ez_data)),\n", " interpolation=\"spline36\",\n", " cmap=\"RdBu\",\n", " alpha=0.9,\n", ")\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](https://meep.readthedocs.io/en/latest/images/directional_coupler_field_profiles.png)\n", "\n", "The field profiles confirm that for `d` of 0.06 μm (Figure 1), the input signal in Port 1 of the top branch is almost completely transferred to Port 4 of the bottom branch. For `d` of 0.13 μm (Figure 2), the input signal is split evenly between the two branches. Finally, for `d` of 0.30 μm (Figure 3), there is no longer any evanescent coupling and the signal remains completely in the top branch. Note the absence of the fields in the PML regions of Ports 3 and 4." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }