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 test_shape_boundary_approximation_in_3D(self):
print(
"3D optimization with shape boundary approximation (use_deps = False): "
)
self.geometry.bounds = [(0.0, self.wg_width / 2.0 - self.mesh_del)
] * len(self.geometry.bounds)
# Note: bounds are tweaked since the shape boundary approximation method does not work
# when the shape under optimization touches the boundary of the FDTD region.
opt = Optimization(base_script=self.base_script +
"setnamed('FDTD','dimension','3D');",
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
hide_fdtd_cad=True,
use_deps=False,
plot_history=False,
store_all_simulations=False)
fom, params = opt.run()
self.assertGreaterEqual(fom, 0.972)
self.assertAlmostEqual(params[0],
(self.wg_width / 2.0 - self.mesh_del) *
self.optimizer.scaling_factor)
self.assertAlmostEqual(params[1],
(self.wg_width / 2.0 - self.mesh_del) *
self.optimizer.scaling_factor)
def test_shape_boundary_approximation(self):
print(
"varFDTD optimization with shape boundary approximation (use_deps = False): "
)
self.geometry.bounds = [
(self.mesh_del, self.wg_width / 2.0 - self.mesh_del)
] * len(self.geometry.bounds)
opt = Optimization(base_script=self.base_script,
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
use_var_fdtd=True,
hide_fdtd_cad=True,
use_deps=False,
plot_history=False,
store_all_simulations=False)
fom, params = opt.run()
self.assertGreaterEqual(fom, 0.972)
self.assertAlmostEqual(params[0],
(self.wg_width / 2.0 - self.mesh_del) *
self.optimizer.scaling_factor)
self.assertAlmostEqual(params[1],
(self.wg_width / 2.0 - self.mesh_del) *
self.optimizer.scaling_factor)
def test_broadband(self):
print("Broadband optimization results:")
opt = Optimization(base_script = self.base_script,
wavelengths = self.wavelengths,
fom = self.fom,
geometry = self.geometry,
optimizer = self.optimizer,
hide_fdtd_cad = True,
use_deps = True)
fom, params = opt.run()
self.assertAlmostEqual(params[0], 2.050400e-7 * self.optimizer.scaling_factor, 5)
self.assertGreaterEqual(fom, 0.4618)
def test_broadband_optimization(self):
print("Broadband optimization results (use_deps = True):")
opt = Optimization(base_script=self.base_script,
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
use_var_fdtd=False,
hide_fdtd_cad=True,
use_deps=True,
plot_history=False,
store_all_simulations=False)
fom, params = opt.run()
self.assertAlmostEqual(params[0],
2.050375e-7 * self.optimizer.scaling_factor[0],
4)
self.assertGreaterEqual(fom, 0.461815)
def test_single_frequency(self):
print("Single frequency optimization results:")
self.fom.target_T_fwd = lambda wl: np.ones(wl.size)
self.fom.multi_freq_src = False
self.wavelengths = 1550.0e-9
self.optimizer.scaling_factor = 2.0e7
self.optimizer.pgtol = 3.1e-2
opt = Optimization(base_script = self.base_script,
wavelengths = self.wavelengths,
fom = self.fom,
geometry = self.geometry,
optimizer = self.optimizer,
hide_fdtd_cad = True,
use_deps = True)
fom, params = opt.run()
self.assertAlmostEqual(params[0], 2.058006e-7 * self.optimizer.scaling_factor, 5)
self.assertGreaterEqual(fom, 0.9192)
def test_permittivity_derivatives_in_2D(self):
print(
"2D optimization with permittivity derivatives (use_deps = True): "
)
opt = Optimization(base_script=self.base_script +
"setnamed('FDTD','dimension','2D');",
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
hide_fdtd_cad=True,
use_deps=True)
fom, params = opt.run()
self.assertGreaterEqual(fom, 0.99991)
self.assertAlmostEqual(
params[0], self.wg_width / 2.0 * self.optimizer.scaling_factor)
self.assertAlmostEqual(
params[1], self.wg_width / 2.0 * self.optimizer.scaling_factor)
def test_permittivity_derivatives(self):
print(
"varFDTD optimization with permittivity derivatives (use_deps = True): "
)
opt = Optimization(base_script=self.base_script,
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
use_var_fdtd=True,
hide_fdtd_cad=True,
use_deps=True,
plot_history=False,
store_all_simulations=False)
fom, params = opt.run()
self.assertGreaterEqual(fom, 0.99991)
self.assertAlmostEqual(
params[0], self.wg_width / 2.0 * self.optimizer.scaling_factor)
self.assertAlmostEqual(
params[1], self.wg_width / 2.0 * self.optimizer.scaling_factor)
def test_single_wavelength_legacy_optimization(self):
print("Single wavelength optimization results (use_deps = False):")
self.fom.target_T_fwd = lambda wl: np.ones(wl.size)
self.fom.multi_freq_src = False
self.wavelengths = 1550.0e-9
self.optimizer.scaling_factor = np.array(2.0e7)
self.optimizer.pgtol = 3.1e-2
opt = Optimization(base_script=self.base_script,
wavelengths=self.wavelengths,
fom=self.fom,
geometry=self.geometry,
optimizer=self.optimizer,
use_var_fdtd=False,
hide_fdtd_cad=True,
use_deps=False,
plot_history=False,
store_all_simulations=False)
fom, params = opt.run()
self.assertAlmostEqual(params[0],
2.05609116e-7 * self.optimizer.scaling_factor,
4)
self.assertGreaterEqual(fom, 0.91905)
depth=wg_height,
eps_out=1.0**2,
eps_in=3.47668**2,
edge_precision=5,
dx=1.0e-5)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name='fom',
mode_number=3,
direction='Backward',
target_T_fwd=lambda wl: 0.5 * np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0,
pgtol=1.0e-4)
######## PUT EVERYTHING TOGETHER ########
opt = Optimization(base_script=base_sim,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
hide_fdtd_cad=False,
use_deps=True)
######## RUN THE OPTIMIZER ########
opt.run()
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()
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()
class LumericalInverseDesign:
"""
Wrapper for Lumerical lumopt (part of it)
Parameters
----------
max_iter: int
method: string
scaling_factor: float
pgtol: float
ftol: float
wavelength_start: float
wavelength_stop: float
wavelength_points: float
build_simulation: string
fom: obj
geometry: obj
hide_fdtd_cad: bool
"""
def __init__(self, max_iter, method, scaling_factor, pgtol, ftol,
wavelength_start, wavelength_stop, wavelength_points,
build_simulation, fom, geometry, hide_fdtd_cad):
# The optimizer must be generated anew at each iteration
self._new_local_optimizer = ScipyOptimizers(
max_iter=max_iter,
method=method,
scaling_factor=scaling_factor,
ftol=ftol,
pgtol=pgtol)
self._wl = Wavelengths(start=wavelength_start,
stop=wavelength_stop,
points=wavelength_points)
self._optimization = Optimization(base_script=build_simulation,
wavelengths=self._wl,
fom=fom,
geometry=geometry,
optimizer=self._new_local_optimizer,
hide_fdtd_cad=hide_fdtd_cad,
use_deps=True)
def run(self):
"""
Run the lumopt optimization
Returns
-------
res: array
The figure of merit of the optimized device
param: numpy array
The optimized parameters
"""
results = self._optimization.run()
self._optimization.sim.fdtd.close()
# plot optimization recap figure
plt.show()
return [results[0], np.array(results[1])]
def _cleanup(self):
''' Remove all the folders generated by lumopt '''
# folder for the file
local_folder = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__)))
subdir_list = os.listdir(local_folder)
for folder in subdir_list:
if folder.startswith('opts_') or (folder.startswith('optimization')
and folder.endswith('.png')):
shutil.rmtree(local_folder + '\\' + folder, ignore_errors=True)
def __del__(self):
# Remove objects to delete pointers or pickle could have problems
del self._optimization
del self._wl
del self._new_local_optimizer
self._cleanup()
