.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/basic/plot_conversion_eff.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_basic_plot_conversion_eff.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_basic_plot_conversion_eff.py:


Polarization conversion
=========================

Simulation of conversion efficiency of a geometric metasurface.

.. GENERATED FROM PYTHON SOURCE LINES 15-21

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import nannos as nn








.. GENERATED FROM PYTHON SOURCE LINES 22-26

The unit cell is a rectangular Si structure on a SiO2 substrate
as in :cite:p:`Yoon2021`.
Circularly-polarized light of wavelength 635 nm is normally incident from the
substrate to the structure

.. GENERATED FROM PYTHON SOURCE LINES 26-36

.. code-block:: Python


    wl = 635  # wavelength
    P = 350  # period
    W = 100  # pillar width along x
    L = 190  # pillar length along y
    eps_Si = (3.87 + 0.02j) ** 2
    eps_SiO2 = 1.4573**2
    nh = 100









.. GENERATED FROM PYTHON SOURCE LINES 37-38

Define a function to initialize simulation

.. GENERATED FROM PYTHON SOURCE LINES 38-52

.. code-block:: Python



    def simu(H, psi):
        lattice = nn.Lattice([[P, 0], [0, P]], discretization=2**9)
        sup = lattice.Layer("Superstrate", epsilon=eps_SiO2)
        sub = lattice.Layer("Substrate", epsilon=1)
        epsilon = lattice.ones()
        metaatom = lattice.rectangle((0.5 * P, 0.5 * P), (W, L))
        epsilon[metaatom] = eps_Si
        ms = lattice.Layer("Metasurface", thickness=H, epsilon=epsilon)
        pw = nn.PlaneWave(wavelength=wl, angles=(0, 0, psi))
        return nn.Simulation([sup, ms, sub], pw, nh=nh)









.. GENERATED FROM PYTHON SOURCE LINES 53-55

Since the layer eigenmodes do not change with thickness we compute them
only once for the first iteration.

.. GENERATED FROM PYTHON SOURCE LINES 55-83

.. code-block:: Python



    nb_thick = 100
    thicknesses = np.linspace(200, 500, nb_thick)
    conv_effs = np.zeros(nb_thick)

    for ih, H in enumerate(thicknesses):
        # x-polarization
        if ih == 0:
            simx = simu(H, 0)
        else:
            simx.layers[1].thickness = H
            simx.reset("S")
        rxi, txi = simx.diffraction_efficiencies(orders=True, complex=True)
        txx = simx.get_order(txi[0], (0, 0))

        # y-polarization
        if ih == 0:
            simy = simu(H, 90)
        else:
            simy.layers[1].thickness = H
            # print(self.is_solved)
            simy.reset("S")
        ryi, tyi = simy.diffraction_efficiencies(orders=True, complex=True)
        tyy = simy.get_order(tyi[1], (0, 0))

        conv_effs[ih] = np.abs((tyy - txx) / 2) ** 2








.. GENERATED FROM PYTHON SOURCE LINES 84-85

Plot the efficiency

.. GENERATED FROM PYTHON SOURCE LINES 85-94

.. code-block:: Python


    plt.clf()
    plt.plot(thicknesses, conv_effs)
    plt.xlabel("H (nm)")
    plt.ylabel("conversion efficiency")
    plt.tight_layout()
    plt.show()





.. image-sg:: /examples/basic/images/sphx_glr_plot_conversion_eff_001.png
   :alt: plot conversion eff
   :srcset: /examples/basic/images/sphx_glr_plot_conversion_eff_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 95-96

Plot the unit cell

.. GENERATED FROM PYTHON SOURCE LINES 96-100

.. code-block:: Python


    p = simx.plot_structure()
    p.show_axes()
    p.show()



.. image-sg:: /examples/basic/images/sphx_glr_plot_conversion_eff_002.png
   :alt: plot conversion eff
   :srcset: /examples/basic/images/sphx_glr_plot_conversion_eff_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 27.391 seconds)

**Estimated memory usage:**  757 MB


.. _sphx_glr_download_examples_basic_plot_conversion_eff.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/nannos/nannos.gitlab.io/doc?filepath=notebooks/examples/basic/plot_conversion_eff.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_conversion_eff.ipynb <plot_conversion_eff.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_conversion_eff.py <plot_conversion_eff.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_conversion_eff.zip <plot_conversion_eff.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_