def create_transformations(
obj: optplan.Function,
monitors: List[optplan.Monitor],
sim_space: optplan.SimulationSpaceBase,
cont_iters: int, # require more to optimise power better
num_stages: int = 3,
min_feature: float = 100,
) -> List[optplan.Transformation]:
"""Creates a list of transformations for the device optimization.
The transformations dictate the sequence of steps used to optimize the
device. The optimization uses `num_stages` of continuous optimization. For
each stage, the "discreteness" of the structure is increased (through
controlling a parameter of a sigmoid function).
Args:
opt: The objective function to minimize.
monitors: List of monitors to keep track of.
sim_space: Simulation space ot use.
cont_iters: Number of iterations to run in continuous optimization
total across all stages.
num_stages: Number of continuous stages to run. The more stages that
are run, the more discrete the structure will become.
min_feature: Minimum feature size in nanometers.
Returns:
A list of transformations.
"""
# Setup empty transformation list.
trans_list = []
# First do continuous relaxation optimization.
# This is done through cubic interpolation and then applying a sigmoid
# function.
param = optplan.CubicParametrization(
# Specify the coarseness of the cubic interpolation points in terms
# of number of Yee cells. Feature size is approximated by having
# control points on the order of `min_feature / GRID_SPACING`.
undersample=3.5 * min_feature / GRID_SPACING,
simulation_space=sim_space,
init_method=optplan.UniformInitializer(min_val=0.6, max_val=0.9),
)
iters = max(cont_iters // num_stages, 1)
for stage in range(num_stages):
trans_list.append(
optplan.Transformation(
name="opt_cont{}".format(stage),
parametrization=param,
transformation=optplan.ScipyOptimizerTransformation(
optimizer="L-BFGS-B",
objective=obj,
monitor_lists=optplan.ScipyOptimizerMonitorList(
callback_monitors=monitors,
start_monitors=monitors,
end_monitors=monitors),
optimization_options=optplan.ScipyOptimizerOptions(
maxiter=iters),
),
))
if stage < num_stages - 1:
# Make the structure more discrete.
trans_list.append(
optplan.Transformation(
name="sigmoid_change{}".format(stage),
parametrization=param,
# The larger the sigmoid strength value, the more "discrete"
# structure will be.
transformation=optplan.CubicParamSigmoidStrength(
value=4 * (stage + 1)),
))
return trans_list
def create_transformations(
obj: optplan.Function,
monitors: List[optplan.Monitor],
sim_space: optplan.SimulationSpaceBase,
cont_iters: int,
num_stages: int = 3,
min_feature: float = 100,
) -> List[optplan.Transformation]:
"""Creates a list of transformations for the device optimization.
The transformations dictate the sequence of steps used to optimize the
device. The optimization uses `num_stages` of continuous optimization. For
each stage, the "discreteness" of the structure is increased (through
controlling a parameter of a sigmoid function).
Args:
obj: The objective function to minimize.
monitors: List of monitors to keep track of.
sim_space: Simulation space ot use.
cont_iters: Number of iterations to run in continuous optimization
total across all stages.
num_stages: Number of continuous stages to run. The more stages that
are run, the more discrete the structure will become.
min_feature: Minimum feature size in nanometers.
fab_obj: Objective including fabrication penalties to be used in the
second half of the optimization
Returns:
A list of transformations.
"""
# Setup empty transformation list.
trans_list = []
# First do continuous relaxation optimization.
# This is done through cubic interpolation and then applying a sigmoid
# function.
param = optplan.CubicParametrization(
# Specify the coarseness of the cubic interpolation points in terms
# of number of Yee cells. Feature size is approximated by having
# control points on the order of `min_feature / GRID_SPACING`.
undersample=3.5 * min_feature / GRID_SPACING,
simulation_space=sim_space,
init_method=optplan.WaveguideInitializer3(lower_min=0,
lower_max=.2,
upper_min=.7,
upper_max=1,
extent_frac_x=1,
extent_frac_y=1 / 2,
center_frac_x=1 / 2,
center_frac_y=1 / 2),
# init_method=optplan.GradientInitializer(min=0, max=1, random=0.3, extent_frac_x=1, extent_frac_y=0.4,
# center_frac_x=0.5, center_frac_y=0.55)
# init_method=optplan.UniformInitializer(min_val=0, max_val=0)
# init_method=optplan.PeriodicInitializer(random=0.2, min=0, max=1, period=400, sim_width=6000,
# center_frac_y=0.5, extent_frac_y=0.4)
)
trans_list.append(
optplan.Transformation(
name="sigmoid_change_init",
parametrization=param,
# The larger the sigmoid strength value, the more "discrete"
# structure will be.
transformation=optplan.CubicParamSigmoidStrength(value=4, )))
iters = max(cont_iters // num_stages, 1)
for stage in range(num_stages):
trans_list.append(
optplan.Transformation(
name="opt_cont{}".format(stage),
parametrization=param,
transformation=optplan.ScipyOptimizerTransformation(
optimizer="L-BFGS-B",
objective=obj,
monitor_lists=optplan.ScipyOptimizerMonitorList(
callback_monitors=monitors,
start_monitors=monitors,
end_monitors=monitors),
optimization_options=optplan.ScipyOptimizerOptions(
maxiter=iters),
),
))
if stage < num_stages - 1:
# Make the structure more discrete.
trans_list.append(
optplan.Transformation(
name="sigmoid_change{}".format(stage),
parametrization=param,
# The larger the sigmoid strength value, the more "discrete"
# structure will be.
transformation=optplan.CubicParamSigmoidStrength(
value=2 * (stage + 3)),
))
return trans_list
