#Semiconductor

This is a place I used to get together a bunch of different analytical models and tabulated data sets for semiconductor properties. Its main focus is Silicon, as that is what I work with.

All the models are implemented in a similar way. A class is built to allows switching of model written by different authors through the author command. The model will then result in the values from the authors implementation. That is, this is module is meant to allow replication of model.

Here is an example of how to use this module. We will look at two different band gap narrowing models. The default model is that from Yan in 2014. model.

    from semiconductor.matterial.bandgap_narrowing import BandGapNarrowing as BGN
    import numpy as np

    # initialise the class
    BGN_class = BGN(matterial='Si')
    # define the number of dopants
    Na = 0.
    Nd = np.logspace(16, 20)
    # Set the excess carriers to zero
    nxc = 0
    bgn_yan = BGN_class.update(Na, Nd, nxc)

If a different band gap narrowing model is desired, pick from the available ones. The available ones can be found using the available_models() function.

    print BGN_class.available_models()

For the band gap narrowing function it returns.

    ['DelAlamo1985', 'Cuevas1996', 'Yan2013bz', 'Yan2014bz', 'Schenk1988fer', 'Schenk1988_reparamitisation_Yan2013', 'Yan2013fer', 'Yan2014fer']

Changing to a model by a different author is done using the author input in either the initalisation of the class, or through the "update" function. Lets choose Schenk's from 1988 and set it through the "update" function. All classes have a similar update function. If we look at the models inputs, we see it also needs an input for temperature. This is just passed to the update function, which passes it to the appropriate places.

    temp = 300
    bgn_sch = BGN_class.update(Na, Nd, nxc, temp=300, author='Schenk1988fer')

Finally we can plot, and compare the differences in the models.

    plt.plot(Nd, bgn_yan, label = 'Yan')
    plt.plot(Nd, bgn_sch, label = 'Schenk')
    plt.legend(loc=0, title='Author')
    plt.xlabel('Doping')
    plt.ylabel('Band Gap Narrowing (eV)')
    plt.semilogx()
    plt.show()