tidy3d.plugins.dispersion.AdvancedFitterParam

class AdvancedFitterParam

Bases: Tidy3dBaseModel

Advanced fitter parameters

Parameters:

• bound_amp (Attribute: bound_amp) –

  Type	Optional[NonNegativeFloat]
  Default	= None
  Units	Hz
  Description	Upper bound of real and imagniary part of oscillator strength c in the model PoleResidue (The default ‘None’ will trigger automatic setup based on the frequency range of interest).

• bound_f (Attribute: bound_f) –

  Type	Optional[NonNegativeFloat]
  Default	= None
  Units	Hz
  Description	Upper bound of real and imaginary part of a that corresponds to pole damping rate and frequency in the model PoleResidue (The default ‘None’ will trigger automatic setup based on the frequency range of interest).

• bound_f_lower (Attribute: bound_f_lower) –

  Type	NonNegativeFloat
  Default	= 0.0
  Units	Hz
  Description	Lower bound of imaginary part of a that corresponds to pole frequency in the model PoleResidue.

• bound_eps_inf (Attribute: bound_eps_inf) –

  Type	ConstrainedFloatValue
  Default	= 10.0
  Description	Upper bound of epsilon at infinity frequency. It must be no less than 1.

• constraint (Attribute: constraint) –

  Type	Literal[‘hard’, ‘soft’]
  Default	= hard
  Description	Stability constraint: ‘hard’ constraints are generally recommended since they are faster to compute per iteration, and they often require fewer iterations to converge since the search space is smaller. But sometimes the search space is so restrictive that all good solutions are missed, then please try the ‘soft’ constraints for larger search space. However, both constraints improve stability equally well.

• nlopt_maxeval (Attribute: nlopt_maxeval) –

  Type	PositiveInt
  Default	= 5000
  Description	Number of iterations in each inner optimization.

• random_seed (Attribute: random_seed) –

  Type	Optional[ConstrainedIntValue]
  Default	= 0
  Description	The fitting tool performs global optimizations with random starting coefficients. With the same random seed, one obtains identical results when re-running the fitter; on the other hand, if one wants to re-run the fitter several times to obtain the best results, the value of the seed should be changed, or set to None so that the starting coefficients are different each time.

Attributes

Methods

bound_amp

bound_f

bound_f_lower

bound_eps_inf

constraint

nlopt_maxeval

random_seed

__hash__()

Hash method.