def adjoint_solver(design_params, mon_type):
matgrid = mp.MaterialGrid(mp.Vector3(Nx,Ny),
mp.air,
silicon,
design_parameters=np.ones((Nx,Ny)))
matgrid_region = mpa.DesignRegion(matgrid,
volume=mp.Volume(center=mp.Vector3(),
size=mp.Vector3(design_shape.x,design_shape.y,0)))
matgrid_geometry = [mp.Block(center=matgrid_region.center,
size=matgrid_region.size,
material=matgrid)]
geometry = waveguide_geometry + matgrid_geometry
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
boundary_layers=boundary_layers,
sources=sources,
geometry=geometry)
if mon_type.name == 'EIGENMODE':
obj_list = [mpa.EigenmodeCoefficient(sim,
mp.Volume(center=mp.Vector3(0.5*sxy-dpml),
size=mp.Vector3(0,sxy,0)),1)]
def J(mode_mon):
return npa.abs(mode_mon)**2
elif mon_type.name == 'DFT':
obj_list = [mpa.FourierFields(sim,
mp.Volume(center=mp.Vector3(0.5*sxy-dpml),
size=mp.Vector3(0,sxy,0)),
mp.Ez)]
def J(mode_mon):
return npa.abs(mode_mon[0,63])**2
opt = mpa.OptimizationProblem(
simulation = sim,
objective_functions = J,
objective_arguments = obj_list,
design_regions = [matgrid_region],
frequencies=[fcen],
decay_fields=[mp.Ez])
f, dJ_du = opt([design_params])
sim.reset_meep()
return f, dJ_du
def adjoint_solver_damping(design_params, frequencies=None, mat2=silicon):
matgrid = mp.MaterialGrid(mp.Vector3(Nx, Ny),
mp.air,
mat2,
weights=np.ones((Nx, Ny)),
damping=3.14 * fcen)
matgrid_region = mpa.DesignRegion(
matgrid,
volume=mp.Volume(center=mp.Vector3(),
size=mp.Vector3(design_region_size.x,
design_region_size.y, 0)))
matgrid_geometry = [
mp.Block(center=matgrid_region.center,
size=matgrid_region.size,
material=matgrid)
]
geometry = waveguide_geometry + matgrid_geometry
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_xy,
sources=wvg_source,
geometry=geometry)
if not frequencies:
frequencies = [fcen]
obj_list = [
mpa.EigenmodeCoefficient(sim,
mp.Volume(
center=mp.Vector3(0.5 * sxy - dpml - 0.1),
size=mp.Vector3(0, sxy - 2 * dpml, 0)),
1,
eig_parity=eig_parity)
]
def J(mode_mon):
return npa.power(npa.abs(mode_mon), 2)
opt = mpa.OptimizationProblem(simulation=sim,
objective_functions=J,
objective_arguments=obj_list,
design_regions=[matgrid_region],
frequencies=frequencies,
minimum_run_time=150)
f, dJ_du = opt([design_params])
sim.reset_meep()
return f, dJ_du
def adjoint_solver_complex_fields(design_params, frequencies=None):
matgrid = mp.MaterialGrid(mp.Vector3(Nx, Ny),
mp.air,
silicon,
weights=np.ones((Nx, Ny)))
matgrid_region = mpa.DesignRegion(
matgrid,
volume=mp.Volume(center=mp.Vector3(),
size=mp.Vector3(design_region_size.x,
design_region_size.y, 0)))
geometry = [
mp.Block(center=matgrid_region.center,
size=matgrid_region.size,
material=matgrid)
]
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
k_point=k_point,
boundary_layers=pml_x,
sources=pt_source,
geometry=geometry)
if not frequencies:
frequencies = [fcen]
obj_list = [
mpa.FourierFields(
sim, mp.Volume(center=mp.Vector3(0.9), size=mp.Vector3(0.2, 0.5)),
mp.Ez)
]
def J(dft_mon):
return npa.power(npa.abs(dft_mon[:, 3, 9]), 2)
opt = mpa.OptimizationProblem(simulation=sim,
objective_functions=J,
objective_arguments=obj_list,
design_regions=[matgrid_region],
frequencies=frequencies)
f, dJ_du = opt([design_params])
sim.reset_meep()
return f, dJ_du
def adjoint_solver(design_params):
design_variables = mp.MaterialGrid(mp.Vector3(Nr, 0, Nz), SiO2, Si)
design_region = mpa.DesignRegion(
design_variables,
volume=mp.Volume(center=mp.Vector3(design_r / 2, 0, 0),
size=mp.Vector3(design_r, 0, design_z)))
geometry = [
mp.Block(center=design_region.center,
size=design_region.size,
material=design_variables)
]
sim = mp.Simulation(cell_size=cell_size,
boundary_layers=boundary_layers,
geometry=geometry,
sources=source,
resolution=resolution,
dimensions=dimensions,
m=m)
far_x = [mp.Vector3(5, 0, 20)]
NearRegions = [
mp.Near2FarRegion(center=mp.Vector3(
design_r / 2, 0, (sz / 2 - dpml + design_z / 2) / 2),
size=mp.Vector3(design_r, 0, 0),
weight=+1)
]
FarFields = mpa.Near2FarFields(sim, NearRegions, far_x)
ob_list = [FarFields]
def J(alpha):
return npa.abs(alpha[0, 0, 0])**2
opt = mpa.OptimizationProblem(simulation=sim,
objective_functions=J,
objective_arguments=ob_list,
design_regions=[design_region],
fcen=fcen,
df=0,
nf=1,
maximum_run_time=1200)
f, dJ_du = opt([design_params])
sim.reset_meep()
return f, dJ_du
TE_top = mpa.EigenmodeCoefficient(sim,
mp.Volume(center=mp.Vector3(0, 1, 0),
size=mp.Vector3(x=2)),
mode=1)
ob_list = [TE_top]
def J(alpha):
return npa.abs(alpha)**2
opt = mpa.OptimizationProblem(simulation=sim,
objective_functions=J,
objective_arguments=ob_list,
design_regions=[design_region],
fcen=fcen,
df=0,
nf=1,
decay_fields=[mp.Ez])
#Plot initial geometry
x0 = 0.5 * np.ones((Nx * Ny, ))
opt.update_design([x0])
opt.plot2D(True)
plt.show()
#Define the cost function
#note the use of the gradient, which is unnecessary for current implementation of the optimizer with GPyOpt
evaluation_history = []