x_res module

This XStep is used to extract resistances from I(V) data. It is explicitly made to be subclassed. Input : I(V) Data Outpout : Resistance parameter with name as specified in the constructor.

Author: Markus Müller Markus.Mueller3@tu-dresden.de

class DMT.extraction.x_res.XRes(name, mcard, lib, op_definition, model, res_name, col_voltage, col_current, **kwargs)[source]

Bases: XStep

Base class for resistance extractions.


Name of this specific xres object.


This parameter collection needs to hold all relevant parameters of the model and is used for simulations or model equation calculations.

libDutLib or list[DutView]

Library or list of devices with the measured resistance.

op_definition{keyfloat, tuple or list}

Defines how to filter the given data in the duts by setting either a single value (float), a range (2-element tuple) or a list of values (list) for each variable to filter.


Model object with all model equations used for this extraction step.


Parameter name for the resistance to be extracted.


Voltage to use for the extraction.


Current to use.

ensure_input_correct_per_dataframe(dataframe, **_kwargs)[source]

Search for all required columns in the data frames.

fit(line, paras_model)[source]
The input data_model is a list of dicts [{‘x’:np.ndarray(), ‘y’:np.ndarray()}].
The x-values are already correct (bounds considered), however this function needs to write the y values.
This method needs to either:
- Calculate the data_model’s y values for the x-values, if the x-step uses a ModelEquation
- Return the data_model’s y values for the x-values, if the x-step uses a dut+sweep combination.
In this cases, XStep already carried out dut+sweep simulations with the parameters before calling the function. Promised.
Reason: This allows to use DMT’s multithreading capabilities, speeding up the extraction significantly.

Return a tex Representation of the Model that is beeing fitted. This can then be displayed in the UI.

init_data_reference_per_dataframe(dataframe, t_meas, dut=None, key=None)[source]

Find the required data in the user supplied dataframe or database and write them into data_model attribute of XStep object. In this case we want to optimize dVBE/dP !


Overwrite main plot.

model_resistance(v=None, res=None, _constant=0, **_kwargs)[source]

Method to extract a resistance from a current/voltage dependence.


Find suitable initial guesses for (some of the) model parameters from the given reference data and in this case also for the x_bounds.

staticMetaObject = PySide6.QtCore.QMetaObject("XRes" inherits "XStep": )[source]