CircuitModel View reference

The model view reference describes the different objects related to circuit modeling.

For a tutorial on defining compact models and circuits, please check the circuit modeling tutorial.

Instances of CircuitModelView

class ipkiss3.all.ModelInstance

Instances to other models.

Parameters:

reference: CompactModelView, required
name: ( String that contains only alphanumeric characters from the ASCII set or contains _$<>. ASCII set is extended on PY3. ), optional, *None allowed*: Unique name of the instance within the scope of the View/Cell
owner: ( _View ), optional, *None allowed*: link to the owner of the instance.In most cases this is automatically added by the _generate_instances method

Defining and testing models and model view

`CircuitModelView`	The CircuitModelView calculates a CompactModel with a circuit solver
`CompactModel`	Base class to define a compact model, which approximates the behavior of a device using an S-matrix or a set of differential equations.
`HierarchicalModel`	A hierarchical model is a model of a circuit, that describes the circuit in terms of model instances, nets (to connect the instances), and terms.

`Probe`	Component that can be placed in a circuit (attached to an existing port) to 'measure' the light.
`FunctionExcitation`	Component that can be placed in a circuit (attached to an existing port) to send a signal into the chip.

`SMatrix1DSweep`	A 3D matrix.
`BSplineSModel`	Numerical S-matrix model consisting of a univariate B-spline representations over wavelength.
`test_circuitmodel`	Test a compact model by running a wavelength sweep.
`map_terms`	Map the terms of a model to different names

Spectrum analysis

High-level interface:

SpectrumAnalyzer and Spectrum give a convenient interface to analyze a group of spectra for different channels of a device or an individual spectrum, respectively.

`SpectrumAnalyzer`	Tool to analyze the transmission spectra of an S-matrix.
`Spectrum`	Object representing a transmission spectrum.

Low-level functions

If SpectrumAnalyzer or Spectrum don’t satisfy your needs you can also use the lower-level analysis functions:

`spectrum_peaks`(smatrix1dsweep[, port_pairs, ...])	Extract information about the peaks in the given S-parameter spectra
`signal_power`(y)	Return the power for each sample in the given spectrum: abs(y)**2
`signal_power_dB`(y)	Return the power in dB for each sample in the given spectrum: 20 * log10(abs(y))
`find_peaks`(x, y[, method, threshold, ...])	Find peaks in data varying in one parameter.
`find_peaks_spline`(x, y[, threshold, smoothing])	Find peaks in data varying in one parameter by means of a spline fit.
`find_peaks_cwt`(x, y[, threshold])	Find peaks in data varying in one parameter by means of a wavelet convolution

Eye Diagram

EyeDiagram

Object representing a signal eyediagram