mirror of
https://github.com/edoverg/modeconverter.git
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464 lines
15 KiB
Plaintext
464 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f65ac12a",
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"metadata": {},
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"source": [
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"This is a first attempt at creating an inverse designed fiber mode converter in meep. At first, we try to implement and combine tutorials from both tidy3d and meep."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7b374471",
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"metadata": {},
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"outputs": [],
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"source": [
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"import meep as mp\n",
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"import meep.adjoint as mpa\n",
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"import numpy as np\n",
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"from autograd import numpy as npa\n",
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"from autograd import tensor_jacobian_product, grad\n",
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"import nlopt\n",
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"from matplotlib import pyplot as plt\n",
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"from matplotlib.patches import Circle\n",
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"import scipy as sp\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3955252a",
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"metadata": {},
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"outputs": [],
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"source": [
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"######PHYSICS SETUP######\n",
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"\n",
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"mp.verbosity(1)\n",
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"\n",
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"#setup a results folder\n",
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"if not os.path.exists(\"results\"):\n",
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" os.makedirs(\"results\")\n",
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"\n",
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"subs_index = 3.4\n",
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"subs_perm = subs_index**2\n",
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"\n",
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"Si = mp.Medium(index = subs_index)\n",
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"Air = mp.Medium(index = 1.0)\n",
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"\n",
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"wavelength = 1.55\n",
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"nf = 3 #number of frequencies to test\n",
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"frequencies = np.array([1/1.5, 1 / 1.55, 1 / 1.6])\n",
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"\n",
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"#distance between PML and source/monitor\n",
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"buffer = 2.5\n",
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"\n",
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"######PML######\n",
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"pml_size = 1.0\n",
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"pml_layers = [mp.PML(pml_size)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b39d1f98",
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"metadata": {},
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"outputs": [],
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"source": [
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"######FILTER######\n",
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"minimum_length = 0.09 # minimum length scale (microns)\n",
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"eta_i = (\n",
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" 0.5 # blueprint (or intermediate) design field thresholding point (between 0 and 1)\n",
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")\n",
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"eta_e = 0.55 # erosion design field thresholding point (between 0 and 1)\n",
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"eta_d = 1 - eta_e # dilation design field thresholding point (between 0 and 1)\n",
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"filter_radius = mpa.get_conic_radius_from_eta_e(minimum_length, eta_e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b1317dd3",
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"metadata": {},
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"outputs": [],
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"source": [
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"######DESIGN REGION######\n",
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"design_region_width = 4.0\n",
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"design_region_length = 4.0\n",
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"design_region_height = 1.0\n",
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"\n",
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"resolution = 15 # pixels/um\n",
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"design_region_resolution = int(resolution)\n",
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"\n",
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"#number of pixels in the design region\n",
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"Nx = int(design_region_width * design_region_resolution) + 1\n",
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"Ny = int(design_region_length * design_region_resolution) + 1\n",
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"\n",
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"design_variables = mp.MaterialGrid(mp.Vector3(Nx, Ny), Air, Si, grid_type=\"U_MEAN\")\n",
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"\n",
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"design_region = mpa.DesignRegion(\n",
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" design_variables,\n",
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" volume=mp.Volume(\n",
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" center = mp.Vector3(0,0,design_region_height / 2),\n",
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" size = mp.Vector3(design_region_width, \n",
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" design_region_length, \n",
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" design_region_height),\n",
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" ),\n",
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")\n",
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"\n",
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"######CELL GEOMETRY######\n",
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"Sx = 2 * pml_size + design_region_width\n",
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"Sy = 2 * pml_size + design_region_length\n",
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"Sz = 2 * pml_size + design_region_height + 2*buffer\n",
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"print(\"Cell size: \", Sx, Sy, Sz)\n",
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"cell_size = mp.Vector3(Sx, Sy, Sz)\n",
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"\n",
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"substrate_thickness = Sz/2\n",
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"substrate_size = mp.Vector3(design_region_width, design_region_length, substrate_thickness)\n",
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"substrate_center = mp.Vector3(\n",
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" 0,\n",
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" 0,\n",
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" - substrate_thickness / 2)\n",
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"\n",
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"substrate_block = mp.Block(\n",
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" center = substrate_center,\n",
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" size = substrate_size,\n",
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" material = Si,\n",
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")\n",
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"\n",
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"design_region_block = mp.Block(\n",
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" center = design_region.center, \n",
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" size = design_region.size, \n",
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" material = design_variables,\n",
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")\n",
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"\n",
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"geometry = [\n",
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" substrate_block,\n",
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" design_region_block,\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "607d05b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"######SOURCE######\n",
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"fcen = 1 / wavelength\n",
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"width = 0.2\n",
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"fwidth = width * fcen\n",
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"source_center = [0, 0, -3]\n",
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"source_size = mp.Vector3(design_region_width, design_region_length, 0)\n",
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"src = mp.GaussianSource(frequency=fcen, fwidth=fwidth)\n",
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"source = [mp.Source(src, component=mp.Ex, size=source_size, center=source_center)]\n",
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"#src = mp.ContinuousSource(frequency=fcen, fwidth=fwidth, is_integrated=True)\n",
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"#source = [mp.Source(src, component=mp.Ex, size=source_size, center=source_center)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cdbe5c87",
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"metadata": {},
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"outputs": [],
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"source": [
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"######SIMULATION######\n",
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"kpoint = mp.Vector3()\n",
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"sim = mp.Simulation(\n",
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" cell_size = cell_size,\n",
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" boundary_layers = pml_layers,\n",
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" geometry = geometry,\n",
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" sources = source,\n",
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" default_material = Air,\n",
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" resolution = resolution,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5f775457",
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"metadata": {},
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"outputs": [],
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"source": [
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"######MONITOR######\n",
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"far_field_obs_point_z_coord = 15\n",
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"far_z = [mp.Vector3(0,0,far_field_obs_point_z_coord)] #this is the far-field-focal-point\n",
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"NearRegions = [\n",
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" mp.Near2FarRegion(\n",
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" center=mp.Vector3(0,0, design_region_height + wavelength),\n",
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" size=mp.Vector3(design_region_width,design_region_length,0),\n",
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" weight=+1,\n",
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" )\n",
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"]\n",
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"#mpa.Near2FarFields return a numpy array with shape (num_of_points,nfreq,6) where the \n",
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"#third axis are the field components Ex,Ey,Ez,Hx,Hy,Hz\n",
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"FarFields = mpa.Near2FarFields(sim,NearRegions,far_z)\n",
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"ob_list = [FarFields]\n",
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"\n",
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"#this the objective to function\n",
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"#the objective is to maximize the obj function,\n",
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"def J1(FF):\n",
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" #in this case we want to maximize field intensity\n",
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" # at the position of the focal spot \n",
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" obj_to_return = npa.mean(npa.abs(FF[0,:,1]) ** 2)\n",
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" return obj_to_return\n",
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"#returns the first point of all frequencies of the Ey component"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "27fb5c57",
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"metadata": {},
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"outputs": [],
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"source": [
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"######CHECK PLOT######\n",
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"output_plane = mp.Volume(\n",
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" center=mp.Vector3(0,0,0), \n",
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" size=mp.Vector3(Sx-2*pml_size,0,Sz-2*pml_size))\n",
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"\n",
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"output_plane_xy = mp.Volume(\n",
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" center=mp.Vector3(0,0,design_region_height / 2),\n",
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" size=mp.Vector3(Sx-2*pml_size,Sy-2*pml_size,0)\n",
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")\n",
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"\n",
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"fig = sim.plot2D(show_sources=True,show_monitors=True,show_epsilon=True, output_plane=output_plane)\n",
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"#sim.run(until=10)\n",
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"#sim.plot2D(fields=mp.Ex,output_plane=output_plane)\n",
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"#plt.savefig(\"fields.pdf\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c576fdb",
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"metadata": {},
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"outputs": [],
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"source": [
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"######OPTIMIZATION PROBLEM######\n",
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"'''\n",
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"An OptimizationProblem class object knows how to do one basic thing: \n",
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"Given an input vector of design variables, compute the objective function value \n",
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"(forward calculation) and optionally its gradient (adjoint calculation)\n",
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"The gradient of the objective function is computed against its arguments, which are\n",
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"the FarFields.\n",
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"'''\n",
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"opt = mpa.OptimizationProblem(\n",
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" simulation=sim, #the optimizer uses this simulation object\n",
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" objective_functions=[J1], #the optimizer call this objective function\n",
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" objective_arguments=ob_list, #the optimizer passes this list of arguments to the objective function\n",
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" design_regions=[design_region], #the optimizer updates the design variables in this design region\n",
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" frequencies=frequencies, #the optimizer runs the simulation at these frequencies\n",
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" maximum_run_time=500, #maximum optimizer run time\n",
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")\n",
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"\n",
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"######CHECK PLOT######\n",
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"figure = opt.plot2D(True, output_plane=output_plane)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b315f21d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def mapping(x,eta,beta):\n",
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" '''\n",
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" Arguments:\n",
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" x: design variables (grey scale)\n",
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" eta: thresholding point for projection (between 0 and 1)\n",
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" beta: binarization strength for projection (positive)\n",
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" Returns:\n",
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" projected_field: the filtered and projected design variables\n",
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" '''\n",
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" #mapping is necessary to reach a physical design.\n",
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" #we start from the design variables (grey scale), which are values in the range (0,1).\n",
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" #typically, we set an initial value of 0.5.\n",
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" #filtering makes a local point behave more like its neighbourhood, avoiding checker patterns\n",
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" filtered_field = mpa.conic_filter(\n",
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" x,\n",
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" filter_radius,\n",
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" design_region_width,\n",
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" design_region_length,\n",
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" [design_region_resolution],\n",
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" )\n",
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"\n",
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" #projection\n",
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" #this is an hyperbolic tangent projection\n",
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" #after filtering, the design is still blurry, so we project it\n",
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" #using an tanh projection method to push the values towards the upper/lower bound\n",
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" #If a filtered value is >0.5, then we push it to 1, otherwise, if <0.5, we push it to 0.\n",
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" #parameter beta represents the degree of binarization,\n",
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" #parameter eta is threshold point\n",
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" projected_field = mpa.tanh_projection(filtered_field,beta,eta)\n",
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" \n",
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" #the following lines introduce symmetry. Need to understand if necessary \n",
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" #projected_field = (\n",
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" # npa.flipud(projected_field) + projected_field\n",
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" #)/2\n",
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"\n",
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" #interpolate to actual materials\n",
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" return projected_field.flatten()\n",
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"\n",
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"evaluation_history = []\n",
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"cur_iter = [0]\n",
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"\n",
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"def f(v, gradient, cur_beta):\n",
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" print(\"current iteration: {}\".format(cur_iter[0]+1))\n",
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"\n",
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" #value and grad\n",
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" f0, dJ_du = opt([mapping(v,eta_i,cur_beta)])\n",
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" if gradient.size > 0:\n",
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" # NOTE: gradient is assigned in-place! No need to return.\n",
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" gradient[...] = tensor_jacobian_product(mapping, 0)(\n",
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" v, eta_i, cur_beta, np.sum(dJ_du, axis=1)\n",
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" )\n",
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" \n",
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" evaluation_history.append(np.real(f0))\n",
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" \n",
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" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
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" \n",
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" opt.plot2D(\n",
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" False, \n",
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" ax=ax1,\n",
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" output_plane=output_plane,\n",
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" show_sources = False,\n",
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" show_monitors = False,\n",
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" show_boundary_layers = False,\n",
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" )\n",
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" circ1 = Circle((2,2), minimum_length/2)\n",
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" ax1.add_patch(circ1)\n",
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" \n",
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" opt.plot2D(\n",
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" False, \n",
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" ax=ax2,\n",
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" output_plane=output_plane_xy,\n",
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" show_sources = False,\n",
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" show_monitors = False,\n",
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" show_boundary_layers = False,\n",
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" )\n",
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" circ2 = Circle((2,2), minimum_length/2)\n",
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" ax2.add_patch(circ2)\n",
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" \n",
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" if not os.path.exists(\"results\"):\n",
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" os.makedirs(\"results\")\n",
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"\n",
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" plt.savefig(\"results/iter_{}.pdf\".format(cur_iter[0]+1))\n",
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"\n",
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" cur_iter[0] += cur_iter[0] + 1\n",
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"\n",
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" return np.real(f0)\n",
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"\n",
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"\n",
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"algorithm = nlopt.LD_MMA\n",
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"n = Nx * Ny #number of parameters to optimize\n",
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"\n",
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"x = np.ones((n,)) * 0.5 #initial guess\n",
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"#x = np.insert(x, 0, -1)\n",
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"#lower and upper bounds for design variables\n",
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"lb = np.zeros((Nx * Ny,))\n",
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"ub = np.ones((Nx * Ny,))\n",
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"\n",
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"cur_beta = 4 #starting beta\n",
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"beta_scale = 2 #beta scaling (update) factor\n",
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"num_betas = 6 #number of betas to test\n",
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"update_factor = 12 #number of iterations per beta\n",
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"#total number of simulations = num_betas * update_factor * 2 (forward + adjoint)\n",
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"ftol = 1e-5\n",
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"\n",
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"optimization_start = True\n",
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"if optimization_start:\n",
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" for iters in range(num_betas):\n",
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" solver = nlopt.opt(algorithm, n)\n",
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" solver.set_lower_bounds(lb)\n",
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" solver.set_upper_bounds(ub)\n",
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" solver.set_max_objective(lambda v, g: f(v, g, cur_beta))\n",
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" solver.set_maxeval(update_factor) #number of iterations for each beta\n",
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" solver.set_ftol_rel(ftol)\n",
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" #observe that x is updated in place, so at\n",
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" # the end it will contain the optimized parameters\n",
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" x[...] = solver.optimize(x)\n",
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" cur_beta = cur_beta * beta_scale #update beta\n",
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" print(\"current beta: \", cur_beta)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "164e57d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"######RESULTS######\n",
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"#plot the figure of merit over iterations\n",
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"plt.figure()\n",
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"plt.plot(evaluation_history)\n",
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"plt.xlabel(\"Iteration\")\n",
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"plt.ylabel(\"Figure of Merit\")\n",
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"plt.grid()\n",
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"plt.savefig(\"results/figure_of_merit.pdf\")\n",
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"\n",
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"#plot and save the final design\n",
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"np.savetxt(\"results/final_design.txt\", mapping(x, eta_i, cur_beta))\n",
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"opt.update_design([mapping(x, eta_i, cur_beta)])\n",
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"\n",
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"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
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" \n",
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"opt.plot2D(\n",
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" False, \n",
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" ax=ax1,\n",
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" output_plane=output_plane,\n",
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" show_sources = False,\n",
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" show_monitors = False,\n",
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" show_boundary_layers = False,\n",
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")\n",
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"\n",
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"circ1 = Circle((2,2), minimum_length/2)\n",
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"ax1.add_patch(circ1)\n",
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"ax1.axis(\"off\")\n",
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"\n",
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"opt.plot2D(\n",
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" False, \n",
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" ax=ax2,\n",
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" output_plane=output_plane_xy,\n",
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" show_sources = False,\n",
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" show_monitors = False,\n",
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" show_boundary_layers = False,\n",
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")\n",
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"circ2 = Circle((2,2), minimum_length/2)\n",
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"ax2.add_patch(circ2)\n",
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"ax2.axis(\"off\")\n",
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" \n",
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"plt.savefig(\"results/final_design_xy.pdf\")\n"
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]
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