The pyhton code is written myself. I based it off of https://optics.ansys.com/hc/en-us/articles/1500007188582-Topology-Optimization-of-a-4-channel-wavelength-demultiplexer-2D-TE. But I can successfully run the example without issue.
I think my code is very similar to the example and I am not sure where I am using the sim attribute. Can you help me identify where I am doing this? Here is my optimization script:
######## IMPORTS ########
# General purpose imports
import numpy as np
import os
import sys
import scipy as sp
# Optimization specific imports
from lumopt import CONFIG
from lumopt.geometries.topology import TopologyOptimization2D, TopologyOptimization3DLayered
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimization import Optimization
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.utilities.wavelengths import Wavelengths
sys.path.append(os.path.dirname(__file__))
from cross_split_base import cross_split_base_init_
cross_split_base = cross_split_base_init_
cur_path = os.path.dirname(os.path.abspath(__file__))
######## RUNS TOPOLOGY OPTIMIZATION OF A 2D STRUCTURE ########
def runSim(params, eps_min, eps_max, x_pos, y_pos, filter_R, working_dir):
######## DEFINE A 2D TOPOLOGY OPTIMIZATION REGION ########
geometry = TopologyOptimization2D(params=params, eps_min=eps_min, eps_max=eps_max, x=x_pos, y=y_pos, z=0, filter_R=filter_R, min_feature_size=filter_R)
######## DEFINE FIGURE OF MERIT ########
# The base simulation script defines a field monitor named 'fom' at the point where we want to modematch to the fundamental TE mode
fom0 = ModeMatch(monitor_name = 'fom 0',
mode_number = 'fundamental TE mode',
direction = 'Backward',
target_T_fwd = lambda wl: 0.25*np.ones(wl.size),
norm_p = 1)
fom1 = ModeMatch(monitor_name = 'fom 1',
mode_number = 'fundamental TE mode',
direction = 'Forward',
target_T_fwd = lambda wl: 0.25*np.ones(wl.size),
norm_p = 1)
fom2 = ModeMatch(monitor_name = 'fom 2',
mode_number = 'fundamental TE mode',
direction = 'Forward',
target_T_fwd = lambda wl: 0.25*np.ones(wl.size),
norm_p = 1)
fom3 = ModeMatch(monitor_name = 'fom 3',
mode_number = 'fundamental TE mode',
direction = 'Backward',
target_T_fwd = lambda wl: 0.25*np.ones(wl.size),
norm_p = 1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=50, method='L-BFGS-B', scaling_factor=1, pgtol=1e-6, ftol=1e-5, scale_initial_gradient_to=0.25)
######## SETTING UP THE OPTIMIZER ########
wavelengths = Wavelengths(start = 1545e-9, stop = 1555e-9, points = 11)
opt0 = Optimization(base_script=cross_split_base, wavelengths = wavelengths, fom=fom0, geometry=geometry, optimizer=optimizer, use_deps=False, use_var_fdtd = False, hide_fdtd_cad=False, plot_history=True, store_all_simulations=False, save_global_index=True)
opt1 = Optimization(base_script=cross_split_base, wavelengths = wavelengths, fom=fom1, geometry=geometry, optimizer=optimizer, use_deps=False, use_var_fdtd = False, hide_fdtd_cad=False, plot_history=True, store_all_simulations=False, save_global_index=False)
opt2 = Optimization(base_script=cross_split_base, wavelengths = wavelengths, fom=fom2, geometry=geometry, optimizer=optimizer, use_deps=False, use_var_fdtd = False, hide_fdtd_cad=False, plot_history=True, store_all_simulations=False, save_global_index=False)
opt3 = Optimization(base_script=cross_split_base, wavelengths = wavelengths, fom=fom3, geometry=geometry, optimizer=optimizer, use_deps=False, use_var_fdtd = False, hide_fdtd_cad=False, plot_history=True, store_all_simulations=False, save_global_index=False)
#opt.continuation_max_iter = 20 #< How many iterations per binarization step (default is 20)
######## RUN THE OPTIMIZER ########
opt = opt0+opt1+opt2+opt3
opt.run(working_dir = working_dir)
if __name__ == '__main__':
size_x = 5000 #< Length of the device (in nm). Longer devices typically lead to better performance
delta_x = 20 #< Size of a pixel along x-axis (in nm)
size_y = 5000 #< Since we use symmetry, this is only have the extent along the y-axis (in nm)
delta_y = 20 #< Size of a pixel along y-axis (in nm)
filter_R = 100 #< Radius of the smoothing filter which removes small features and sharp corners (in nm)
eps_max = 2.848152**2 #< Effective permittivity for a Silicon waveguide with a thickness of 220nm
eps_min = 1.44**2 #< Permittivity of the SiO2 cladding
x_points=int(size_x/delta_x)+1
y_points=int(size_y/delta_y)+1
x_pos = np.linspace(-size_x/2,size_x/2,x_points)*1e-9
y_pos = np.linspace(-size_y/2,size_y/2,y_points)*1e-9
## Set initial conditions
initial_cond = 0.5*np.ones((x_points,y_points)) #< Start with the domain filled with (eps_max+eps_min)/2
## Alternative initial conditions
#initial_cond = None #< Use the structure 'initial_guess' as defined in the project file
#initial_cond = np.ones((x_points,y_points)) #< Start with the domain filled with eps_max
#initial_cond = np.zeros((x_points,y_points)) #< Start with the domain filled with eps_min
working_dir = os.path.join(cur_path, 'cross_split_x{:04d}_y{:04d}_f{:04d}'.format(size_x,size_y,int(filter_R)))
runSim(initial_cond, eps_min, eps_max, x_pos, y_pos, filter_R*1e-9, working_dir)
You are navigating away from the AIS Discovery experience
