2.3. More interesting systems: spin, potential, shape

Each of the following three examples highlights different ways to go beyond the very simple examples of the previous section.

Matrix structure of on-site and hopping elements

See also

You can execute the code examples live in your browser by activating thebelab:

Activate Thebelab

See also

The complete source code of this example can be found in spin_orbit.py

We begin by extending the simple 2DEG-Hamiltonian by a Rashba spin-orbit coupling and a Zeeman splitting due to an external magnetic field:

\[H = \frac{-\hbar^2}{2 m} (\partial_x^2+\partial_y^2) - i \alpha (\partial_x \sigma_y - \partial_y \sigma_x) + E_\text{Z} \sigma_z + V(y)\]

Here \(\sigma_{x,y,z}\) denote the Pauli matrices.

It turns out that this well studied Rashba-Hamiltonian has some peculiar properties in (ballistic) nanowires: It was first predicted theoretically in Phys. Rev. Lett. 90, 256601 (2003) that such a system should exhibit non-monotonic conductance steps due to a spin-orbit gap. Only very recently, this non-monotonic behavior has been supposedly observed in experiment: Nature Physics 6, 336 (2010). Here we will show that a very simple extension of our previous examples will exactly show this behavior (Note though that no care was taken to choose realistic parameters).

The tight-binding model corresponding to the Rashba-Hamiltonian naturally exhibits a 2x2-matrix structure of onsite energies and hoppings. In order to use matrices in our program, we import the Tinyarray package. (NumPy would work as well, but Tinyarray is much faster for small arrays.)

# Tutorial 2.3.1. Matrix structure of on-site and hopping elements
# ================================================================
#
# Physics background
# ------------------
#  Gaps in quantum wires with spin-orbit coupling and Zeeman splititng,
#  as theoretically predicted in
#   https://doi.org/10.1103/PhysRevLett.90.256601
#  and (supposedly) experimentally oberved in
#   https://doi.org/10.1038/nphys1626
#
# Kwant features highlighted
# --------------------------
#  - Numpy matrices as values in Builder

import kwant

# For plotting
from matplotlib import pyplot

import matplotlib
import matplotlib.pyplot
from matplotlib_inline.backend_inline import set_matplotlib_formats

matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
set_matplotlib_formats('svg')

# For matrix support
import tinyarray

For convenience, we define the Pauli-matrices first (with \(\sigma_0\) the unit matrix):

# define Pauli-matrices for convenience
sigma_0 = tinyarray.array([[1, 0], [0, 1]])
sigma_x = tinyarray.array([[0, 1], [1, 0]])
sigma_y = tinyarray.array([[0, -1j], [1j, 0]])
sigma_z = tinyarray.array([[1, 0], [0, -1]])

and we also define some other parameters useful for constructing our system:

t = 1.0
alpha = 1.0
e_z = 0.08
W, L = 10, 30

Previously, we used numbers as the values of our matrix elements. However, Builder also accepts matrices as values, and we can simply write:

lat = kwant.lattice.square(norbs=2)
syst = kwant.Builder()

#### Define the scattering region. ####
syst[(lat(x, y) for x in range(L) for y in range(W))] = \
    4 * t * sigma_0 + e_z * sigma_z
# hoppings in x-direction
syst[kwant.builder.HoppingKind((1, 0), lat, lat)] = \
    -t * sigma_0 + 1j * alpha * sigma_y / 2
# hoppings in y-directions
syst[kwant.builder.HoppingKind((0, 1), lat, lat)] = \
    -t * sigma_0 - 1j * alpha * sigma_x / 2

Note that we specify norbs=2 when creating the lattice, as each site has 2 degrees of freedom associated with it, giving us 2x2 matrices as onsite/hopping terms. Note that the Zeeman energy adds to the onsite term, whereas the Rashba spin-orbit term adds to the hoppings (due to the derivative operator). Furthermore, the hoppings in x and y-direction have a different matrix structure. We now cannot use lat.neighbors() to add all the hoppings at once, since we now have to distinguish x and y-direction. Because of that, we have to explicitly specify the hoppings in the form expected by HoppingKind:

• A tuple with relative lattice indices. For example, (1, 0) means hopping from (i, j) to (i+1, j), whereas (1, 1) would mean hopping to (i+1, j+1).

• The target lattice (where to hop to)

• The source lattice (where the hopping originates)

Since we are only dealing with a single lattice here, source and target lattice are identical, but still must be specified (for an example with hopping between different (sub)lattices, see Beyond square lattices: graphene).

Again, it is enough to specify one direction of the hopping (i.e. when specifying (1, 0) it is not necessary to specify (-1, 0)), Builder assures hermiticity.

The leads also allow for a matrix structure,

#### Define the left lead. ####
lead = kwant.Builder(kwant.TranslationalSymmetry((-1, 0)))

lead[(lat(0, j) for j in range(W))] = 4 * t * sigma_0 + e_z * sigma_z
# hoppings in x-direction
lead[kwant.builder.HoppingKind((1, 0), lat, lat)] = \
    -t * sigma_0 + 1j * alpha * sigma_y / 2
# hoppings in y-directions
lead[kwant.builder.HoppingKind((0, 1), lat, lat)] = \
    -t * sigma_0 - 1j * alpha * sigma_x / 2

#### Attach the leads and finalize the system. ####
syst.attach_lead(lead)
syst.attach_lead(lead.reversed())
syst = syst.finalized()

The remainder of the code is unchanged, and as a result we should obtain the following, clearly non-monotonic conductance steps:

# Compute conductance
energies=[0.01 * i - 0.3 for i in range(100)]
data = []
for energy in energies:
    smatrix = kwant.smatrix(syst, energy)
    data.append(smatrix.transmission(1, 0))

pyplot.figure()
pyplot.plot(energies, data)
pyplot.xlabel("energy [t]")
pyplot.ylabel("conductance [e^2/h]")
pyplot.show()

Technical details

• The Tinyarray package, one of the dependencies of Kwant, implements

  efficient small arrays. It is used internally in Kwant for storing small vectors and matrices. For performance, it is preferable to define small arrays that are going to be used with Kwant using Tinyarray. However, NumPy would work as well:

  import numpy
  sigma_0 = numpy.array([[1, 0], [0, 1]])
  sigma_x = numpy.array([[0, 1], [1, 0]])
  sigma_y = numpy.array([[0, -1j], [1j, 0]])
  sigma_z = numpy.array([[1, 0], [0, -1]])
  

  Tinyarray arrays behave for most purposes like NumPy arrays except that they are immutable: they cannot be changed once created. This is important for Kwant: it allows them to be used directly as dictionary keys.

• It should be emphasized that the relative hopping used for

  HoppingKind is given in terms of lattice indices, i.e. relative to the Bravais lattice vectors. For a square lattice, the Bravais lattice vectors are simply (a,0) and (0,a), and hence the mapping from lattice indices (i,j) to real space and back is trivial. This becomes more involved in more complicated lattices, where the real-space directions corresponding to, for example, (1,0) and (0,1) need not be orthogonal any more (see Beyond square lattices: graphene).

Spatially dependent values through functions

See also

The complete source code of this example can be found in quantum_well.py

# Tutorial 2.3.2. Spatially dependent values through functions
# ============================================================
#
# Physics background
# ------------------
#  transmission through a quantum well
#
# Kwant features highlighted
# --------------------------
#  - Functions as values in Builder

import kwant

# For plotting
from matplotlib import pyplot

import matplotlib
import matplotlib.pyplot
from matplotlib_inline.backend_inline import set_matplotlib_formats

matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
set_matplotlib_formats('svg')

Up to now, all examples had position-independent matrix-elements (and thus translational invariance along the wire, which was the origin of the conductance steps). Now, we consider the case of a position-dependent potential:

\[H = \frac{\hbar^2}{2 m} (\partial_x^2+\partial_y^2) + V(x, y)\]

The position-dependent potential enters in the onsite energies. One possibility would be to again set the onsite matrix elements of each lattice point individually (as in Transport through a quantum wire). However, changing the potential then implies the need to build up the system again.

Instead, we use a python function to define the onsite energies. We define the potential profile of a quantum well as:

W, L, L_well = 10, 30, 10

def potential(site, pot):
    (x, y) = site.pos
    if (L - L_well) / 2 < x < (L + L_well) / 2:
        return pot
    else:
        return 0

This function takes two arguments: the first of type Site, from which you can get the real-space coordinates using site.pos, and the value of the potential as the second. Note that in potential we can access variables L and L_well that are defined globally.

Kwant now allows us to pass a function as a value to Builder:

a = 1
t = 1.0

def onsite(site, pot):
    return 4 * t + potential(site, pot)

lat = kwant.lattice.square(a, norbs=1)
syst = kwant.Builder()

syst[(lat(x, y) for x in range(L) for y in range(W))] = onsite
syst[lat.neighbors()] = -t

#### Define and attach the leads. ####
lead = kwant.Builder(kwant.TranslationalSymmetry((-a, 0)))
lead[(lat(0, j) for j in range(W))] = 4 * t
lead[lat.neighbors()] = -t
syst.attach_lead(lead)
syst.attach_lead(lead.reversed())

syst = syst.finalized()

For each lattice point, the corresponding site is then passed as the first argument to the function onsite. The values of any additional parameters, which can be used to alter the Hamiltonian matrix elements at a later stage, are specified later during the call to smatrix. Note that we had to define onsite, as it is not possible to mix values and functions as in syst[...] = 4 * t + potential.

For the leads, we just use constant values as before. If we passed a function also for the leads (which is perfectly allowed), this function would need to be compatible with the translational symmetry of the lead – this should be kept in mind.

Finally, we compute the transmission probability:

def plot_conductance(syst, energy, welldepths):

    # Compute conductance
    data = []
    for welldepth in welldepths:
        smatrix = kwant.smatrix(syst, energy, params=dict(pot=-welldepth))
        data.append(smatrix.transmission(1, 0))

    pyplot.figure()
    pyplot.plot(welldepths, data)
    pyplot.xlabel("well depth [t]")
    pyplot.ylabel("conductance [e^2/h]")
    pyplot.show()

kwant.smatrix allows us to specify a dictionary, params, that contains the additional arguments required by the Hamiltonian matrix elements. In this example we are able to solve the system for different depths of the potential well by passing the potential value (remember above we defined our onsite function that takes a parameter named pot). We obtain the result:

plot_conductance(syst, energy=0.2,
                 welldepths=[0.01 * i for i in range(100)])

Starting from no potential (well depth = 0), we observe the typical oscillatory transmission behavior through resonances in the quantum well.

Warning

If functions are used to set values inside a lead, then they must satisfy the same symmetry as the lead does. There is (currently) no check and wrong results will be the consequence of a misbehaving function.

Technical details :class: dropdown note

• Functions can also be used for hoppings. In this case, they take two Site’s as arguments and then an arbitrary number of additional arguments.

• Apart from the real-space position pos, Site has also an attribute tag containing the lattice indices of the site.

Nontrivial shapes

See also

The complete source code of this example can be found in ab_ring.py

# Tutorial 2.3.3. Nontrivial shapes
# =================================
#
# Physics background
# ------------------
#  Flux-dependent transmission through a quantum ring
#
# Kwant features highlighted
# --------------------------
#  - More complex shapes with lattices
#  - Allows for discussion of subtleties of `attach_lead` (not in the
#    example, but in the tutorial main text)
#  - Modifcations of hoppings/sites after they have been added

from cmath import exp
from math import pi

import kwant

# For plotting
from matplotlib import pyplot

import matplotlib
import matplotlib.pyplot
from matplotlib_inline.backend_inline import set_matplotlib_formats

matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
set_matplotlib_formats('svg')

Up to now, we only dealt with simple wire geometries. Now we turn to the case of a more complex geometry, namely transport through a quantum ring that is pierced by a magnetic flux \(\Phi\):

For a flux line, it is possible to choose a gauge such that a charged particle acquires a phase \(e\Phi/h\) whenever it crosses the branch cut originating from the flux line (branch cut shown as red dashed line) [1]. There are more symmetric gauges, but this one is most convenient to implement numerically.

Defining such a complex structure adding individual lattice sites is possible, but cumbersome. Fortunately, there is a more convenient solution: First, define a boolean function defining the desired shape, i.e. a function that returns True whenever a point is inside the shape, and False otherwise:

r1, r2 = 10, 20

def ring(pos):
    (x, y) = pos
    rsq = x ** 2 + y ** 2
    return (r1 ** 2 < rsq < r2 ** 2)

Note that this function takes a real-space position as argument (not a Site).

We can now simply add all of the lattice points inside this shape at once, using the function shape provided by the lattice:

a = 1
t = 1.0

lat = kwant.lattice.square(a, norbs=1)
syst = kwant.Builder()

syst[lat.shape(ring, (0, r1 + 1))] = 4 * t
syst[lat.neighbors()] = -t

Here, lat.shape takes as a second parameter a (real-space) point that is inside the desired shape. The hoppings can still be added using lat.neighbors() as before.

Up to now, the system contains constant hoppings and onsite energies, and we still need to include the phase shift due to the magnetic flux. This is done by overwriting the values of hoppings in x-direction along the branch cut in the lower arm of the ring. For this we select all hoppings in x-direction that are of the form (lat(1, j), lat(0, j)) with j<0:

# In order to introduce a flux through the ring, we introduce a phase on
# the hoppings on the line cut through one of the arms.  Since we want to
# change the flux without modifying the Builder instance repeatedly, we
# define the modified hoppings as a function that takes the flux as its
# parameter phi.
def hopping_phase(site1, site2, phi):
    return -t * exp(1j * phi)

def crosses_branchcut(hop):
    ix0, iy0 = hop[0].tag

    # builder.HoppingKind with the argument (1, 0) below
    # returns hoppings ordered as ((i+1, j), (i, j))
    return iy0 < 0 and ix0 == 1  # ix1 == 0 then implied

# Modify only those hopings in x-direction that cross the branch cut
def hops_across_cut(syst):
    for hop in kwant.builder.HoppingKind((1, 0), lat, lat)(syst):
        if crosses_branchcut(hop):
            yield hop

syst[hops_across_cut] = hopping_phase

Here, crosses_branchcut is a boolean function that returns True for the desired hoppings. We then use again a generator (this time with an if-conditional) to set the value of all hoppings across the branch cut to fluxphase. The rationale behind using a function instead of a constant value for the hopping is again that we want to vary the flux through the ring, without constantly rebuilding the system – instead the flux is governed by the parameter phi.

For the leads, we can also use the lat.shape-functionality:

#### Define the leads. ####
W = 10

sym_lead = kwant.TranslationalSymmetry((-a, 0))
lead = kwant.Builder(sym_lead)


def lead_shape(pos):
    (x, y) = pos
    return (-W / 2 < y < W / 2)

lead[lat.shape(lead_shape, (0, 0))] = 4 * t
lead[lat.neighbors()] = -t

Here, the shape must be compatible with the translational symmetry of the lead sym_lead. In particular, this means that it should extend to infinity along the translational symmetry direction (note how there is no restriction on x in lead_shape) [2].

Attaching the leads is done as before:

#### Attach the leads ####
syst.attach_lead(lead)
syst.attach_lead(lead.reversed())

In fact, attaching leads seems not so simple any more for the current structure with a scattering region very much different from the lead shapes. However, the choice of unit cell together with the translational vector allows to place the lead unambiguously in real space – the unit cell is repeated infinitely many times in the direction and opposite to the direction of the translational vector. Kwant examines the lead starting from infinity and traces it back (going opposite to the direction of the translational vector) until it intersects the scattering region. At this intersection, the lead is attached:

After the lead has been attached, the system should look like this:

kwant.plot(syst);

The computation of the conductance goes in the same fashion as before. Finally you should get the following result:

def plot_conductance(syst, energy, fluxes):
    # compute conductance

    normalized_fluxes = [flux / (2 * pi) for flux in fluxes]
    data = []
    for flux in fluxes:
        smatrix = kwant.smatrix(syst, energy, params=dict(phi=flux))
        data.append(smatrix.transmission(1, 0))

    pyplot.figure()
    pyplot.plot(normalized_fluxes, data)
    pyplot.xlabel("flux [flux quantum]")
    pyplot.ylabel("conductance [e^2/h]")
    pyplot.show()

# We should see a conductance that is periodic with the flux quantum
plot_conductance(syst.finalized(), energy=0.15,
                 fluxes=[0.01 * i * 3 * 2 * pi for i in range(100)])

where one can observe the conductance oscillations with the period of one flux quantum.

Technical details :class: dropdown note

• Leads have to have proper periodicity. Furthermore, the Kwant format requires the hopping from the leads to the scattering region to be identical to the hoppings between unit cells in the lead. attach_lead takes care of all these details for you! In fact, it even adds points to the scattering region, if proper attaching requires this. This becomes more apparent if we attach the leads a bit further away from the central axis o the ring, as was done in this example:

  import kwant
  from matplotlib import pyplot
  

  import matplotlib
  import matplotlib.pyplot
  from matplotlib_inline.backend_inline import set_matplotlib_formats
  
  matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
  set_matplotlib_formats('svg')
  

  a = 1
  t = 1.0
  W = 10
  r1, r2 = 10, 20
  
  lat = kwant.lattice.square(norbs=1)
  syst = kwant.Builder()
  def ring(pos):
      (x, y) = pos
      rsq = x**2 + y**2
      return ( r1**2 < rsq < r2**2)
  syst[lat.shape(ring, (0, 11))] = 4 * t
  syst[lat.neighbors()] = -t
  sym_lead0 = kwant.TranslationalSymmetry((-a, 0))
  lead0 = kwant.Builder(sym_lead0)
  def lead_shape(pos):
      (x, y) = pos
      return (-1 < x < 1) and ( 0.5 * W < y < 1.5 * W )
  lead0[lat.shape(lead_shape, (0, W))] = 4 * t
  lead0[lat.neighbors()] = -t
  lead1 = lead0.reversed()
  syst.attach_lead(lead0)
  syst.attach_lead(lead1)
  
  kwant.plot(syst);
  

  

• Per default, attach_lead attaches the lead to the “outside” of the structure, by tracing the lead backwards, coming from infinity.

  One can also attach the lead to the inside of the structure, by providing an alternative starting point from where the lead is traced back:

  syst.attach_lead(lead1, lat(0, 0))
  

  starts the trace-back in the middle of the ring, resulting in the lead being attached to the inner circle:

  import kwant
  from matplotlib import pyplot
  

  import matplotlib
  import matplotlib.pyplot
  from matplotlib_inline.backend_inline import set_matplotlib_formats
  
  matplotlib.rcParams['figure.figsize'] = matplotlib.pyplot.figaspect(1) * 2
  set_matplotlib_formats('svg')
  

  a = 1
  t = 1.0
  W = 10
  r1, r2 = 10, 20
  
  lat = kwant.lattice.square(a, norbs=1)
  syst = kwant.Builder()
  def ring(pos):
      (x, y) = pos
      rsq = x**2 + y**2
      return ( r1**2 < rsq < r2**2)
  syst[lat.shape(ring, (0, 11))] = 4 * t
  syst[lat.neighbors()] = -t
  sym_lead0 = kwant.TranslationalSymmetry((-a, 0))
  lead0 = kwant.Builder(sym_lead0)
  def lead_shape(pos):
      (x, y) = pos
      return (-1 < x < 1) and ( -W/2 < y < W/2  )
  lead0[lat.shape(lead_shape, (0, 0))] = 4 * t
  lead0[lat.neighbors()] = -t
  lead1 = lead0.reversed()
  syst.attach_lead(lead0)
  syst.attach_lead(lead1, lat(0, 0))
  
  kwant.plot(syst);
  

  

  Note that here the lead is treated as if it would pass over the other arm of the ring, without intersecting it.

Footnotes

[1]

The corresponding vector potential is \(A_x(x,y)=\Phi \delta(x) \Theta(-y)\) which yields the correct magnetic field \(B(x,y)=\Phi \delta(x)\delta(y)\).

[2]

Despite the “infinite” shape, the unit cell will still be finite; the TranslationalSymmetry takes care of that.