tidy3d.SteadyConvergenceData

class SteadyConvergenceData

Bases: Tidy3dBaseModel

Per-bias Newton convergence state for a steady-state charge sweep.

Parameters:

• converged (Optional[SteadyVoltageDataArray] = None) – True at biases where the Newton iteration met the configured tolerance and False otherwise.

• n_iters (Optional[SteadyVoltageDataArray] = None) – Number of Newton iterations taken at each bias. At a non-converged bias this equals the cap if max_iters was hit, or the count of attempted steps before divergence.

• residual_history (Optional[ConvergenceHistoryDataArray] = None) – Per-bias trace of the absolute residual norm. Dimensions are (v, pseudo_step, component); ragged biases are NaN-padded along pseudo_step to the longest trace – use n_iters for the unpadded length per bias.

Example

>>> import tidy3d as td
>>> V = [-1, 0, 1]
>>> converged = td.SteadyVoltageDataArray(data=[1, 1, 0], coords={"v": V})
>>> n_iters = td.SteadyVoltageDataArray(data=[42, 38, 100], coords={"v": V})
>>> conv = SteadyConvergenceData(converged=converged, n_iters=n_iters)

Attributes

converged
n_iters
residual_history

converged

n_iters

residual_history