tidy3d.plugins.invdes.FilterProject#
- class FilterProject[source]#
Bases:
InvdesBaseModel
Transformation involving convolution by a conic filter followed by a
tanh
projection.- Parameters:
attrs (dict = {}) – Dictionary storing arbitrary metadata for a Tidy3D object. This dictionary can be freely used by the user for storing data without affecting the operation of Tidy3D as it is not used internally. Note that, unlike regular Tidy3D fields,
attrs
are mutable. For example, the following is allowed for setting anattr
obj.attrs['foo'] = bar
. Also note that Tidy3D` will raise aTypeError
ifattrs
contain objects that can not be serialized. One can check ifattrs
are serializable by callingobj.json()
.radius (PositiveFloat) – [units = um]. Radius of the filter to convolve with supplied spatial data. Note: the corresponding feature size expressed in the device is typically sqrt(3) times smaller than the radius. For best results, it is recommended to make your radius about twice as large as the desired feature size. Note: the radius value is often only approximately related to the final feature sizes. It is useful to apply a
FilterProject
transformation to ‘encourage’ larger feature sizes, but we ultimately recommend creating aErosionDilationPenalty
to theDesignRegion.penalties
if you have strict fabrication constraints.beta (ConstrainedFloatValue = 1.0) – Steepness of the binarization, higher means more sharp transition at the expense of gradient accuracy and ease of optimization.
eta (ConstrainedFloatValue = 0.5) – Halfway point in projection function.
strict_binarize (bool = False) – If
False
, the binarization is still continuous between min and max. IfTrue
, the values are snapped to the min and max values after projection.
Notes
Attributes
Methods
evaluate
(spatial_data, design_region_dl)Evaluate this transformation on spatial data, given some grid size in the region.
Inherited Common Usage
- radius#
- beta#
- eta#
- strict_binarize#
- evaluate(spatial_data, design_region_dl)[source]#
Evaluate this transformation on spatial data, given some grid size in the region.
- __hash__()#
Hash method.