def runSim(params,
eps_bg,
eps_wg,
x_pos,
y_pos,
size_x,
filter_R,
beta_start=1):
######## DEFINE A 2D TOPOLOGY OPTIMIZATION REGION ########
geometry = TopologyOptimization2D(params=params,
eps_min=eps_bg,
eps_max=eps_wg,
x=x_pos,
y=y_pos,
z=0,
filter_R=filter_R,
beta=beta_start)
######## 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
fom = ModeMatch(monitor_name='fom',
mode_number='Fundamental TE mode',
direction='Forward',
norm_p=2)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=50,
method='L-BFGS-B',
scaling_factor=1,
pgtol=1e-6,
ftol=1e-4,
target_fom=0.5,
scale_initial_gradient_to=0.25)
######## LOAD TEMPLATE SCRIPT AND SUBSTITUTE PARAMETERS ########
script = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/Ysplitter/splitter_base_2D_TE_topology.lsf'))
script = script.replace('opt_size_x=3.5e-6',
'opt_size_x={:1.6g}'.format(size_x))
wavelengths = Wavelengths(start=1450e-9, stop=1650e-9, points=11)
opt = Optimization(base_script=script,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
use_deps=False,
hide_fdtd_cad=True,
plot_history=False,
store_all_simulations=False)
######## RUN THE OPTIMIZER ########
opt.run()
def setUp(self):
# base script
self.base_script = load_from_lsf(os.path.join(self.file_dir, 'modematch_parallel_plate_waveguide_TM_base.lsf'))
# bandwidth
self.wavelengths = Wavelengths(start = 1540e-9, stop = 1560e-9, points = 3)
# simulation
self.sim = Simulation(workingDir = self.file_dir, hide_fdtd_cad = True)
self.sim.fdtd.eval(self.base_script)
Optimization.set_global_wavelength(self.sim, self.wavelengths)
# reference
self.ref_fom = 0.6643986
def setUp(self):
# Base simulation script
self.base_script = load_from_lsf(
os.path.join(self.file_dir,
'optimization_parallel_plate_waveguide_TE_base.lsf'))
# Simulation bandwidth
self.wavelengths = Wavelengths(start=1500e-9, stop=1600e-9, points=11)
# Polygon defining a rectangle that can grow or shrink along the y-axis to fill the gap
self.mesh_del = 10.0e-9
# must be kept in sych with self.base_script
initial_points_y = np.array(
[0.01 * self.mesh_del, 1.75 * self.mesh_del])
def wall(param=initial_points_y):
assert param.size == 2, "walls defined by two points."
self.wg_gap = 10.0 * self.mesh_del # must be kept in synch
points_x = 0.5 * np.array(
[-self.wg_gap, self.wg_gap, self.wg_gap, -self.wg_gap])
points_y = np.array([-param[0], -param[1], param[1], param[0]])
polygon_points = [(x, y) for x, y in zip(points_x, points_y)]
return np.array(polygon_points)
self.wg_width = 50.0 * self.mesh_del # must be kept in synch
bounds = [(0.0, self.wg_width / 2.0)] * initial_points_y.size
self.geometry = FunctionDefinedPolygon(
func=wall,
initial_params=initial_points_y,
bounds=bounds,
z=0.0, # must be kept in synch
depth=self.wg_width, # must be kept in synch
eps_out=1.0**2, # must be kept in synch with
eps_in=Material(base_epsilon=4.0**2,
name='<Object defined dielectric>',
mesh_order=1), # must be kept in sych with
edge_precision=50,
dx=1.0e-10)
# Figure of merit
self.fom = ModeMatch(
monitor_name='fom', # must be kept in sych
mode_number=1, # must be kept in sych
direction='Forward',
multi_freq_src=True,
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
# Scipy optimizer
self.optimizer = ScipyOptimizers(max_iter=5,
method='L-BFGS-B',
scaling_factor=1.0e6,
pgtol=1.0e-5,
ftol=1.0e-12,
target_fom=0.0,
scale_initial_gradient_to=None)
def _load_simulation_file(self, file):
""" Load a simulation file """
# Assumes file is either an absolute path or accessible in the
# current directory
if '.fsp' in file and os.path.isfile(file):
self.fdtd.load(file)
elif '.lsf' in file and os.path.isfile(file):
script_str = load_from_lsf(file)
self._script_eval(script_str)
else:
raise UserWarning('Input file must be either .fsp or .lsf')
def __init__(self, script_obj):
if callable(script_obj):
self.callable_obj = script_obj
params = signature(script_obj).parameters
if len(params) > 1:
raise UserWarning(
'function to create base simulation must take a single argument (handle to FDTD CAD).'
)
elif isinstance(script_obj, str):
if '.fsp' in script_obj and os.path.isfile(script_obj):
self.project_file = os.path.abspath(script_obj)
elif '.lsf' in script_obj and os.path.isfile(script_obj):
self.script_str = load_from_lsf(os.path.abspath(script_obj))
else:
self.script_str = str(script_obj)
else:
raise UserWarning(
'object for generating base simulation must be a Python function, a file name or a string with a Lumerical script.'
)
def setUp(self):
# Base simulation script
self.base_script = load_from_lsf(os.path.join(self.file_dir, 'optimization_waveguide_filter_TM_2D_base.lsf'))
# Simulation bandwidth
self.wavelengths = Wavelengths(start = 1300e-9,
stop = 1800e-9,
points = 41)
# Polygons to form the two gaps
self.mesh_del = 20.0e-9; # must be kept in sych with self.base_script
initial_param = 10.0 * np.array([self.mesh_del])
def rectangle(param = initial_param, offset = 0.0):
assert param.size == 1, "rectangle grows along a single dimension."
wg_width = 35.0 * self.mesh_del # must be kept in synch
points_x = 0.5 * np.array([-wg_width, wg_width, wg_width, -wg_width])
points_y = 0.5 * np.array([-param, -param, param, param]) + offset
polygon_points = [(x, y) for x, y in zip(points_x, points_y)]
return np.array(polygon_points)
bounds = [(self.mesh_del, 20.0 * self.mesh_del)]
z = 0.0 # must be kept in sych
depth = 200.0 * self.mesh_del # must be kept in sych
eps_in = Material(base_epsilon = 1.44 ** 2, mesh_order = 1) # must be kept in sych with
eps_out = Material(base_epsilon = 2.8 ** 2, mesh_order = 1) # must be kept in sych with
edge_precision = 25
dx = 1.0e-10
self.geometry = (FunctionDefinedPolygon(func = lambda param: rectangle(param[0], 2.0 * param[0]), initial_params = initial_param, bounds = bounds, z = z, depth = depth, eps_out = eps_out, eps_in = eps_in, edge_precision = edge_precision, dx = dx) *
FunctionDefinedPolygon(func = lambda param: rectangle(param[0],-2.0 * param[0]), initial_params = initial_param, bounds = bounds, z = z, depth = depth, eps_out = eps_out, eps_in = eps_in, edge_precision = edge_precision, dx = dx))
# Broadband figure of merit
target_T_fwd = lambda wl: 0.3 + 0.65*np.power(np.sin(np.pi * (wl - wl.min()) / (wl.max() - wl.min())), 6)
self.fom = ModeMatch(monitor_name = 'FOM', # must be kept in sych
mode_number = 1, # must be kept in sych
direction = 'Backward',
multi_freq_src = True,
target_T_fwd = target_T_fwd,
norm_p = 1)
# Scipy optimzier
self.optimizer = ScipyOptimizers(max_iter = 10,
method = 'L-BFGS-B',
scaling_factor = 1.0e7,
pgtol = 5.6e-3)
import os
import numpy as np
import scipy as sp
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.utilities.materials import Material
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
script = load_from_lsf(
os.path.join(os.path.dirname(__file__),
'splitter_base_TE_modematch_3D.lsf'))
######## DEFINE SPECTRAL RANGE #########
wavelengths = Wavelengths(start=1300e-9, stop=1800e-9, points=21)
######## DEFINE DATABASE MATERIALS #####
silicon = Material(name='Si (Silicon) - Palik', mesh_order=2)
######## DEFINE OPTIMIZABLE GEOMETRY ########
initial_points_x = np.linspace(-1.0e-6, 1.0e-6, 10)
initial_points_y = 0.25e-6 + (0.6e-6 - 0.25e-6) * np.power(
np.sin(np.pi / 2.0 * (initial_points_x - initial_points_x.min()) /
(initial_points_x.max() - initial_points_x.min())), 2)
# General purpose imports
import os
import numpy as np
import scipy as sp
from lumopt import CONFIG
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
base_script = load_from_lsf(
os.path.join(os.path.dirname(__file__), 'splitter_with_arms.lsf'))
######## DEFINE SPECTRAL RANGE #########
wavelengths = Wavelengths(start=1550e-9, stop=1550e-9, points=1)
######## DEFINE OPTIMIZABLE GEOMETRY ########
# The class FunctionDefinedPolygon needs a parameterized Polygon (with points ordered
# in a counter-clockwise direction). Here the geometry is defined by 10 parameters defining
# the knots of a spline, and the resulting Polygon has 200 edges, making it quite smooth.
def taper_splitter(params=np.linspace(0.25e-6, 2e-6, 20)):
''' Defines a taper where the paramaters are the y coordinates of the nodes of a cubic spline. '''
points_x = np.concatenate(([-2.51e-6], np.linspace(-2.5e-6, 2.5e-6,
20), [2.51e-6]))
points_y = np.concatenate(([0.25e-6], params, [2e-6]))
def runGratingOptimization(bandwidth_in_nm, etch_depth, n_grates, params):
bounds = [(0.1, 1)] * 4
bounds[0] = (-3, 3) #< Starting position
bounds[1] = (0, 0.1) #< Scaling parameter R
bounds[2] = (1.5, 3) #< Parameter a
bounds[3] = (0, 2) #< Parameter b
def grating_params_pos(params,
output_waveguide_length=0.5e-6,
height=220e-9,
y0=0):
x_begin = -3e-6
y3 = y0 + height
y1 = y3 - etch_depth
x_start = params[0] * 1e-6 #< First parameter is the starting position
x0 = x_start
R = params[1] * 1e6 #< second parameter (unit is 1/um)
a = params[2] #< Third parameter (dim-less)
b = params[3] #< Fourth parameter (dim-less)
verts = np.array([[x_begin, y0], [x_begin, y3], [x0, y3], [x0, y1]])
lambda_c = 1.55e-6
F0 = 0.95
## Iterate over all but the last
for i in range(n_grates - 1):
F = F0 - R * (x0 - x_start)
Lambda = lambda_c / (a + F * b)
x1 = x0 + (1 - F) * Lambda #< Width of the etched region
x2 = x0 + Lambda #< Rest of cell
verts = np.concatenate(
(verts, [[x1, y1], [x1, y3], [x2, y3], [x2, y1]]), axis=0)
x0 = x2
F = F0 - R * (x0 - x_start)
Lambda = lambda_c / (a + F * b)
x1 = x0 + (1 - F) * Lambda #< Width of the etched region
x_end = x1 + output_waveguide_length
verts = np.concatenate(
(verts, [[x1, y1], [x1, y3], [x_end, y3], [x_end, y0]]), axis=0)
return verts
geometry = FunctionDefinedPolygon(func=grating_params_pos,
initial_params=params,
bounds=bounds,
z=0.0,
depth=110e-9,
eps_out=1.44**2,
eps_in=3.47668**2,
edge_precision=5,
dx=1e-3)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name='fom',
mode_number=1,
direction='Backward',
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=25,
method='L-BFGS-B',
scaling_factor=1,
pgtol=1e-6)
######## DEFINE BASE SIMULATION ########
base_script = load_from_lsf(
os.path.join(os.path.dirname(__file__),
'grating_coupler_2D_2etch.lsf'))
######## PUT EVERYTHING TOGETHER ########
lambda_start = 1550 - bandwidth_in_nm / 2
lambda_end = 1550 + bandwidth_in_nm / 2
lambda_pts = int(bandwidth_in_nm / 10) + 1
wavelengths = Wavelengths(start=lambda_start * 1e-9,
stop=lambda_end * 1e-9,
points=lambda_pts)
opt = Optimization(base_script=base_script,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
hide_fdtd_cad=True,
use_deps=True)
######## RUN THE OPTIMIZER ########
opt.run()
with open('script_file.lsf', 'a') as file:
file.write(self.base_script.replace(';', ';\n'))
if __name__ == '__main__':
import numpy as np
from lumopt.geometries.polygon import function_defined_Polygon, cross
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
import os
import matplotlib.pyplot as plt
from lumopt import CONFIG
base_script = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/crossing/crossing_base_TE_modematch_2D.lsf'))
fom = ModeMatch(modeorder=2)
optimizer = ScipyOptimizers(max_iter=20)
# optimizer=FixedStepGradientDescent(max_dx=20e-9,max_iter=100)
bounds = [(0.2e-6, 1e-6)] * 10
geometry = function_defined_Polygon(func=cross,
initial_params=np.linspace(
0.25e-6, 0.6e-6, 10),
eps_out='SiO2 (Glass) - Palik',
eps_in=2.8**2,
bounds=bounds,
depth=220e-9,
edge_precision=5)
import numpy as np
from lumopt.optimization import Super_Optimization, Optimization
from lumopt.geometries.polygon import function_defined_Polygon
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
import os
from lumopt import CONFIG
import scipy
######## DEFINE BASE SIMULATION ########
#Here I just use the same script for both simulations, but it's just to keep the example simple. You could use two
script_1 = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/Ysplitter/splitter_base_TE_modematch.lsf'))
script_2 = load_from_lsf(
os.path.join(
CONFIG['root'],
'examples/Ysplitter/splitter_base_TE_modematch_25nmoffset.lsf'))
######## DEFINE OPTIMIZABLE GEOMETRY ########
## Here the two splitters just have a 25nm offset from each other, so that the result is robust
def taper_splitter_1(params, n_points=10):
points_x = np.concatenate(([-1.01e-6], np.linspace(-1.1e-6, 0.9e-6,
10), [1.01e-6]))
points_y = np.concatenate(([0.25e-6], params, [0.6e-6]))
n_interpolation_points = 100
######## IMPORTS ########
# General purpose imports
import os
import numpy as np
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.utilities.materials import Material
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
script = load_from_lsf(
os.path.join(os.path.dirname(__file__), 'grating_base.lsf'))
######## DEFINE SPECTRAL RANGE #########
wavelengths = Wavelengths(start=1300e-9, stop=1800e-9, points=21)
######## DEFINE MATERIALS ##############
oxide = Material(1.44**2, mesh_order=1)
silicon = Material(3.4**2)
######## DEFINE OPTIMIZABLE GEOMETRY ########
n_grates = 22
def grate_function_generator(grate_number,
grate_height=70e-9,
grate_center_y=220e-9 - 35e-9):
def runGratingOptimization(bandwidth_in_nm, etch_depth_shallow, etch_depth_deep, n_grates, initial_params = None):
### Yet another parametrization which allows to enforce minimum feature size when the optimizer only supports box constraints
### params = [x0, a1, b1, ..., aN]
if initial_params is None:
params = np.zeros(4*n_grates)
for i in range(n_grates):
params[i*4] = 0.2 #< Width up
params[i*4+1] = 0.4*(i/n_grates) #< Width of the shallow etch
params[i*4+2] = 0.1 #< Width up
params[i*4+3] = 0.4*(i/n_grates) #< Width of the deep etch
params[0] = 0 #< Overwrite the first since it has a special meaning: Start of the grating at 0um
else:
params = initial_params
bounds = [(0, 1)]*(4*n_grates)
bounds[0] = (-3,3)
def grating_params_pos(params, output_waveguide_length = 0.5e-6, height = 220e-9, y0 = 0):
x_begin = -3e-6
y3 = y0+height
y2 = y3-etch_depth_deep
y1 = y3-etch_depth_shallow
x0 = params[0]*1e-6 #< First parameter is the starting position
verts = np.array( [ [x_begin,y0],[x_begin,y3],[x0,y3],[x0,y1] ] )
## Iterate over all but the last
for i in range(n_grates-1):
x1 = x0 + params[i*4+1]*1e-6 #< Width of the deep etch
x2 = x1 + params[i*4+2]*1e-6 #< Width up
x3 = x2 + params[i*4+3]*1e-6 #< Width of the shallow etch
x4 = x3 + params[i*4+4]*1e-6 #< Width up
verts = np.concatenate((verts,[[x1,y1],[x1,y3],[x2,y3],[x2,y2],[x3,y2],[x3,y3],[x4,y3],[x4,y1]]),axis=0)
x0 = x4
x1 = x0 + params[(n_grates-1)*4+1]*1e-6 #< Width of the deep etch
x2 = x1 + params[(n_grates-1)*4+2]*1e-6 #< Width up
x3 = x2 + params[(n_grates-1)*4+3]*1e-6 #< Width of the shallow etch
x_end = x3+output_waveguide_length
verts = np.concatenate((verts,[[x1,y1],[x1,y3],[x2,y3],[x2,y2],[x3,y2],[x3,y3],[x_end,y3],[x_end,y0]]),axis=0)
return verts
geometry = FunctionDefinedPolygon(func = grating_params_pos, initial_params = params, bounds = bounds, z = 0.0, depth = 220e-9, eps_out = 1.44 ** 2, eps_in = 3.47668 ** 2, edge_precision = 5, dx = 1e-3)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name = 'fom', mode_number = 1, direction = 'Backward', target_T_fwd = lambda wl: np.ones(wl.size), norm_p = 1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter = 250, method = 'L-BFGS-B', scaling_factor = 1, pgtol = 1e-6) #SLSQP
######## DEFINE BASE SIMULATION ########
base_script = load_from_lsf(os.path.join(os.path.dirname(__file__), 'grating_coupler_2D_2etch.lsf'))
######## PUT EVERYTHING TOGETHER ########
lambda_start = 1550 - bandwidth_in_nm/2
lambda_end = 1550 + bandwidth_in_nm/2
lambda_pts = int(bandwidth_in_nm/10)+1
wavelengths = Wavelengths(start = lambda_start*1e-9, stop = lambda_end*1e-9, points = lambda_pts)
opt = Optimization(base_script = base_script, wavelengths = wavelengths, fom = fom, geometry = geometry, optimizer = optimizer, hide_fdtd_cad = True, use_deps = True)
######## RUN THE OPTIMIZER ########
opt.run()
import numpy as np
import scipy as sp
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE SPECTRAL RANGE #########
wavelengths = Wavelengths(start = 1550e-9, stop = 1550e-9, points = 1)
######## DEFINE BASE SIMULATION ########
# Use the same script for both simulations, but it's just to keep the example simple. You could use two.
script_1 = load_from_lsf(os.path.join(os.path.dirname(__file__), 'splitter_base_TE_modematch.lsf'))
script_2 = load_from_lsf(os.path.join(os.path.dirname(__file__), 'splitter_base_TE_modematch_25nmoffset.lsf'))
######## DEFINE OPTIMIZABLE GEOMETRY ########
## Here the two splitters just have a 25nm offset from each other, so that the result is robust
def taper_splitter_1(params,n_points=10):
points_x = np.concatenate(([-1.01e-6],np.linspace(-1.1e-6,0.9e-6,10),[1.01e-6]))
points_y = np.concatenate(([0.25e-6],params,[0.6e-6]))
n_interpolation_points=100
polygon_points_x = np.linspace(min(points_x), max(points_x), n_interpolation_points)
interpolator = sp.interpolate.interp1d(points_x, points_y, kind='cubic')
polygon_points_y = [max(min(point,1e-6),-1e-6) for point in interpolator(polygon_points_x)]
polygon_points_up = [(x, y) for x, y in zip(polygon_points_x, polygon_points_y)]
polygon_points_down = [(x, -y) for x, y in zip(polygon_points_x, polygon_points_y)]
import os
import numpy as np
import scipy as sp
from lumopt import CONFIG
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
base_script_file_name = os.path.join(
CONFIG['root'], 'examples/Ysplitter/splitter_with_arms.lsf')
base_script = load_from_lsf(base_script_file_name)
######## DEFINE OPTIMIZABLE GEOMETRY ########
# The class FunctionDefinedPolygon needs a parameterized Polygon (with points ordered
# in a counter-clockwise direction). Here the geometry is defined by 10 parameters defining
# the knots of a spline, and the resulting Polygon has 200 edges, making it quite smooth.
def taper_splitter(params=np.linspace(0.25e-6, 2e-6, 20)):
''' Defines a taper where the paramaters are the y coordinates of the nodes of a cubic spline. '''
points_x = np.concatenate(([-2.51e-6], np.linspace(-2.5e-6, 2.5e-6,
20), [2.51e-6]))
points_y = np.concatenate(([0.25e-6], params, [2e-6]))
n_interpolation_points = 100
px = np.linspace(min(points_x), max(points_x), n_interpolation_points)
interpolator = sp.interpolate.interp1d(points_x, points_y, kind='cubic')
######## IMPORTS ########
# General purpose imports
import os
import numpy as np
import scipy as sp
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
# Use the same script for both simulations, but it's just to keep the example simple. You could use two.
script_1550 = load_from_lsf(
os.path.join(os.path.dirname(__file__), 'WDM_splitter_base_TE_1550.lsf'))
script_1310 = load_from_lsf(
os.path.join(os.path.dirname(__file__),
'WDM_splitter_base_TE_1550.lsf')).replace(
'1550e-9', '1310e-9')
######## DEFINE SPECTRAL RANGE #########
wavelengths_1551 = Wavelengths(start=1550e-9, stop=1550e-9, points=1)
wavelengths_1310 = Wavelengths(start=1310e-9, stop=1310e-9, points=1)
######## DEFINE OPTIMIZABLE GEOMETRY ########
separation = 500.0e-9
size_x = 10.0e-6
def lower_coupler_arm(params, n_points=10):
import numpy as np
import os
import scipy
from lumopt import CONFIG
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.geometries.polygon import function_defined_Polygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimization import Optimization
from lumopt.optimizers.generic_optimizers import ScipyOptimizers, FixedStepGradientDescent
from lumopt.utilities.materials import Material
######## DEFINE BASE SIMULATION ########
script = load_from_lsf(
os.path.join(CONFIG['root'], 'examples/grating/grating_base.lsf'))
######## DEFINE OPTIMIZABLE GEOMETRY ########
# The class function_defined_Polygon needs a parameterized Polygon (with points ordered
# in a counter-clockwise direction. This funtion will automatically generate all the polygons needed
n_grates = 22
def grate_function_generator(grate_number,
grate_height=70e-9,
grate_center_y=220e-9 - 35e-9):
def grate_function(params):
grate_position = params[grate_number]
grate_width = params[grate_number + len(params) / 2]
# General purpose imports
import numpy as np
from lumopt.optimization import Super_Optimization, Optimization
from lumopt.geometries.polygon import function_defined_Polygon
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
import os
from lumopt import CONFIG
import scipy
######## DEFINE BASE SIMULATION ########
#Here I just use the same script for both simulations, but it's just to keep the example simple. You could use two
script_1550=load_from_lsf(os.path.join(CONFIG['root'],'examples/WDM_splitter/WDM_splitter_base_TE_1550.lsf'))
script_1310=load_from_lsf(os.path.join(CONFIG['root'],'examples/WDM_splitter/WDM_splitter_base_TE_1550.lsf')).replace('1550e-9','1310e-9')
######## DEFINE OPTIMIZABLE GEOMETRY ########
separation=500e-9
size_x=10e-6
def lower_coupler_arm(params,n_points=10):
points_x=np.concatenate(([0.5e-6],np.linspace(0.55e-6,size_x-0.55e-6,20),[size_x-0.5e-6]))
points_y=np.concatenate(([-separation/2],params-separation/2,params[::-1]-separation/2,[-separation/2]))
n_interpolation_points=100
polygon_points_x = np.linspace(min(points_x), max(points_x), n_interpolation_points)
interpolator = scipy.interpolate.interp1d(points_x, points_y, kind='cubic')
polygon_points_y = [max(min(point,0e-6),-separation/2-0.25e-6) for point in interpolator(polygon_points_x)]
######## IMPORTS ########
import os
import numpy as np
import scipy as sp
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.optimization import Optimization
######## DEFINE BASE SIMULATION ########
crossing_base = load_from_lsf(
os.path.join(os.path.dirname(__file__),
'crossing_base_TE_modematch_2D.lsf'))
######## DEFINE SPECTRAL RANGE #########
wavelengths = Wavelengths(start=1300e-9, stop=1800e-9, points=21)
######## DEFINE OPTIMIZABLE GEOMETRY ########
def cross(params):
y_end = params[-1]
x_end = 0 - y_end
points_x = np.concatenate(([-2.01e-6], np.linspace(-2e-6, x_end, 10)))
points_y = np.concatenate(([0.25e-6], params))
n_interpolation_points = 50
polygon_points_x = np.linspace(min(points_x), max(points_x),
n_interpolation_points)
if __name__=='__main__':
import matplotlib as mpl
# mpl.use('TkAgg')
import numpy as np
# from lumopt.figures_of_merit.modematch_importsource import ModeMatch
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimization import Optimization
from lumopt.optimizers.generic_optimizers import ScipyOptimizers, FixedStepGradientDescent
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
import os
from lumopt.geometries.polygon import function_defined_Polygon
from lumopt.utilities.materials import Material
from lumopt import CONFIG
import scipy
script = load_from_lsf(os.path.join(CONFIG['root'], 'examples/staight_waveguide/straight_waveguide.lsf'))
fom = ModeMatch(modeorder=2, precision=50)
optimizer = ScipyOptimizers(max_iter=20)
nx=401
ny=101
eps = np.ones((nx, ny))*1.44 ** 2
eps[90, 10] = 10
geometry = ContinousEpsilon2D(eps=eps, x=np.linspace(-1e-6, 1e-6, nx), y=np.linspace(-0.4e-6, 0.4e-6, ny))
# function_defined_Polygon(func=waveguide, initial_params=np.linspace(0.25e-6, 0.25e-6, 10),
# eps_out=Material(1.44 ** 2), eps_in=Material(2.8 ** 2, 2), bounds=bounds,
# depth=220e-9,
# edge_precision=5)
# geometry=Polygon(eps_in=2.8**2,eps_out=1.44**2)
opt = Optimization(base_script=script, fom=fom, geometry=geometry, optimizer=optimizer)
import numpy as np
import os
import scipy
from lumopt import CONFIG
# Optimization specific imports
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.geometries.polygon import function_defined_Polygon
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.optimization import Optimization
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
######## DEFINE BASE SIMULATION ########
script = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/Ysplitter/splitter_base_TE_modematch_3D.lsf'))
######## DEFINE OPTIMIZABLE GEOMETRY ########
# The class function_defined_Polygon needs a parameterized Polygon (with points ordered
# in a counter-clockwise direction. Here the geometry is defined by 10 parameters defining
# the knots of a spline, and the resulting Polygon has 200 edges, making it quite smooth
def taper_splitter(params=np.linspace(0.25e-6, 0.6e-6, 10)):
'''Just a taper where the parameters are the y coordinates of the nodes of a cubic spline'''
points_x = np.concatenate(([-1.01e-6], np.linspace(-1e-6, 1e-6,
10), [1.01e-6]))
points_y = np.concatenate(([0.25e-6], params, [0.6e-6]))
# General purpose imports
import os
import numpy as np
import scipy as sp
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
from lumopt.utilities.wavelengths import Wavelengths
from lumopt.geometries.polygon import FunctionDefinedPolygon
from lumopt.figures_of_merit.modematch import ModeMatch
from GLOptimizer import *
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from GLOptimization import Optimization
######## DEFINE BASE SIMULATION ########
# Use the same script for both simulations, but it's just to keep the example simple. You could use two.
script_1550 = load_from_lsf(os.path.join(os.path.dirname(__file__), 'grating_test.lsf'))
######## DEFINE SPECTRAL RANGE #########
wavelengths_1550 = Wavelengths(start = 1550e-9, stop = 1550e-9, points = 1)
######## DEFINE OPTIMIZABLE GEOMETRY ########
## Definition Start
def make_polygon_func(center_x,center_y,i):
def make_rectangle(params,n_points = 16):
wg_width = 0.5e-6
#print(params)
width = (params[i]*1000000 + 1)*0.035e-6
points_x = np.array([center_x - width/2, center_x + width/2, center_x + width/2 , center_x - width/2])
points_y = np.array([center_y - wg_width/2 , center_y - wg_width/2 , center_y + wg_width/2 , center_y + wg_width/2])
polygon_points = np.array([(x, y) for x, y in zip(points_x, points_y)])
