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