Rigorous coupled wave analysis supporting automatic differentation with autograd
Project description
grcwa
grcwa (autoGradable RCWA) is a python implementation of rigorous coupled wave analysis (RCWA) for arbitrarily shaped photonic crystal slabs, supporting automatic differentation with autograd
Free software: GPL license
Documentation: https://grcwa.readthedocs.io.
Citing
If you find grcwa useful for your research, please cite the following paper:
@article{Jin2020, title = {Inverse design of lightweight broadband reflector for efficient lightsail propulsion}, author ={Jin, Weiliang and Li, Wei and Orenstein, Meir and Fan, Shanhui}, year = {2020}, journal = {arXiv:2005.04840}, }
Features
RCWA solves EM-scattering problems of stacked photonic crystal slabs. As illustrated in the above figure, the photonic structure can have N layers of different thicknesses and independent spatial dielectric profiles. All layers are periodic in the two lateral directions, and invariant along the vertical direction.
Each photonic crystal layer can have arbitrary dielectric profile on the 2D grids.
autograd is integrated into the package, allowing for automated and fast gradient evaluations for the sake of large-scale optimizations. Autogradable parameters include dielectric constant on every grid, frequency, angles, thickness of each layer, and periodicity (however the ratio of periodicity along the two lateral directions must be fixed).
Quick Start
Installation:
$ pip install grcwa
Or,
$ git clone git://github.com/weiliangjinca/grcwa $ pip install .
Example 1: transmission and reflection (sum or by order) of a square lattice of a hole: ex1.py
Example 2: Transmission and reflection of two patterned layers: ex2.py, as illustrated in the figure below (only a unit cell is plotted)
Periodicity in the lateral direction is Lx = Ly = 0.2, and frequency is 1.0.
The incident light has an angel pi/10.
import grcwa import numpy as np grcwa.set_backend('autograd') # if autograd needed # lattice constants L1 = [0.2,0] L2 = [0,0.2] # Truncation order (actual number might be smaller) nG = 101 # frequency freq = 1. # angle theta = np.pi/10 phi = 0. # setup RCWA obj = grcwa.obj(nG,L1,L2,freq,theta,phi,verbose=1)
Geometry: the thicknesses of the four layers are 0.1,0.2,0.3, and 0.4. For patterned layers, we consider total grid points Nx * Ny = 100*100 within the unit cell.
Dielectric constant: 2.0 for the 0-th layer; 4.0 (1.0) for the 1st layer in the orange (void) region; 6.0 (1.0) for the 2nd layer in the bule (void) region; and 3.0 for the last layer.
Np = 2 # number of patterned layers Nx = 100 Ny = 100 thick0 = 0.1 pthick = [0.2,0.3] thickN = 0.4 ep0 = 2. epN = 3. obj.Add_LayerUniform(thick0,ep0) for i in range(Np): obj.Add_LayerGrid(pthick[i],Nx,Ny) obj.Add_LayerUniform(thickN,epN) obj.Init_Setup()
Patterned layer: the 1-th layer a circular hole of radius 0.5 Lx, and the 2-nd layer has a square hole of 0.5 Lx
radius = 0.5 a = 0.5 ep1 = 4. ep2 = 6. epbkg = 1. # coordinate x0 = np.linspace(0,1.,Nx) y0 = np.linspace(0,1.,Ny) x, y = np.meshgrid(x0,y0,indexing='ij') # layer 1 epgrid1 = np.ones((Nx,Ny))*ep1 ind = (x-.5)**2+(y-.5)**2<radius**2 epgrid1[ind]=epbkg # layer 2 epgrid2 = np.ones((Nx,Ny))*ep2 ind = np.logical_and(np.abs(x-.5)<a/2 and np.abs(y-.5)<a/2)) epgrid2[ind]=epbkg # combine epsilon of all layers epgrid = np.concatenate((epgrid1.flatten(),epgrid2.flatten())) obj.GridLayer_geteps(epgrid)
Incident light is s-polarized
planewave={'p_amp':0,'s_amp':1,'p_phase':0,'s_phase':0} obj.MakeExcitationPlanewave(planewave['p_amp'],planewave['p_phase'],planewave['s_amp'],planewave['s_phase'],order = 0) # solve for R and T R,T= obj.RT_Solve(normalize=1)
Example 3: topology optimization of reflection of a single patterned layer, ex3.py
Note on conventions
The vacuum permittivity, permeability, and speed of light are 1.
The time harmonic convention is exp(-i omega t).
Acknowledgements
My implementation of RCWA received helpful discussions from Dr. Zin Lin. Many details of implementations were referred to a RCWA package implemented in c called S4. The idea of integrating Autograd into RCWA package rather than deriving adjoint-variable gradient by hand was inspired by a discussion with Dr. Ian Williamson and Dr. Momchil Minkov. The backend and many other styles follow their implementation in legume. Haiwen Wang and Cheng Guo provided useful feedback. Lastly, the template was credited to Cookiecutter and the audreyr/cookiecutter-pypackage.
History
0.1.1 (2020-05-18)
Fix license
0.1 (2020-05-12)
First release on PyPI.
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