Material Dispersion

In this example, we will perform a simulation with a frequency-dependent dielectric ε(ω), corresponding to material dispersion. See Materials for more information on how material dispersion is supported. In particular, we will model a uniform medium of the dispersive material. See also From the dispersion relation , we will compute the numerical ε(ω) via the formula:

We will then compare this with the analytical ε(ω) that we specified.

Since this is a uniform medium, our computational cell can actually be of zero size (i.e. one pixel), where we will use Bloch-periodic boundary conditions to specify the wavevector k.

cell = mp.Vector3()
resolution = 20

We will then fill all space with a dispersive material:

susceptibilities = [mp.LorentzianSusceptibility(frequency=1.1, gamma=1e-5, sigma=0.5),
                    mp.LorentzianSusceptibility(frequency=0.5, gamma=0.1, sigma=2e-5)]

default_material = mp.Medium(epsilon=2.25, E_susceptibilities=susceptibilities)

corresponding to the dielectric function:

The real and imaginary parts of this dielectric function ε(ω) are plotted below:

We can see that the f=1.1 resonance causes a large change in both the real and imaginary parts of ε around that frequency. In fact, there is a range of frequencies from 1.1 to 1.2161 where ε is negative. In this range, no propagating modes exist — it is actually a kind of electromagnetic band gap associated with polariton resonances in a material. For more information on the physics of such materials, see e.g. Chapter 14 of Introduction to Solid State Physics by C. Kittel.

On the other hand, the f=0.5 resonance, because the sigma numerator is so small, causes very little change in the real part of ε. Nevertheless, it generates a clear peak in the imaginary part of ε, corresponding to a resonant absorption peak.

Now, we'll set up the rest of the simulation. We'll specify a broadband -polarized Gaussian source, create a list of k wavevectors that we want to compute over, and compute the associated frequencies by using the k_points function:

fcen = 1.0
df = 2.0

sources = [mp.Source(mp.GaussianSource(fcen, fwidth=df), component=mp.Ez, center=mp.Vector3())]

kmin = 0.3
kmax = 2.2
k_interp = 99

kpts = mp.interpolate(k_interp, [mp.Vector3(kmin), mp.Vector3(kmax)])

sim = mp.Simulation(cell_size=cell, geometry=[], sources=sources, default_material=default_material, resolution=resolution)

all_freqs =, k_points=200)  # a list of lists of frequencies

The k_points function returns a list of lists of frequencies — one list of complex frequencies for each k point — which we store in the all_freqs variable. Finally, we want to loop over this list and print out the corresponding ε via the ratio as described above. To do this, we will use Python's zip function which combines multiple lists into one:

for fs, kx in zip(all_freqs, [v.x for v in kpts]):
    for f in fs:
            print("eps:, {.6f}, {.6f}, {.6f}".format(f.real, f.imag, (kx / f)**2))

Alternatively we could just read all of the frequencies into Octave/Matlab or a spreadsheet and compute the ratios there. After running the program with

unix% python | tee material-dispersion.out

we can then grep for the frequencies and the computed dielectric function, and plot it. First, let's plot the dispersion relation ω(k) for the real part of ω:

The red circles are the computed points from Meep, whereas the blue line is the analytical band diagram from the specified ε(ω). As you can see, we get two bands at each k, separated by a polaritonic gap (shaded yellow). This dispersion relation can be thought of as the interaction (anti-crossing) between the light line of the ambient ε=2.25 material (dashed black line) and the horizontal line corresponding to the phonon resonance.

Similarly, the computed and analytical real parts of the dielectric function are given by:

which shows excellent agreement between the analytical (blue line) and numerical (red circles) calculations. The imaginary part, however, is more subtle:

The blue line is the analytical calculation from above and the red circles are the numerical value from Meep — why is the agreement so poor? There is nothing wrong with Meep, and this is not a numerical error. The problem is simply that we are comparing apples and oranges.

The blue line is the analytical calculation of ε(ω) for a real frequency ω which corresponds to solutions with a complex wavevector k, whereas Meep is computing ε at a complex ω for a real wavevector k. So, the correct comparison is to plug Meep's complex ω into the analytical formula for ε(ω), which results in the green lines on the graph that fall almost on top of the red circles.

Why did our comparison of the real part of ε look so good, then? The reason is that ε(ω) at real and complex values of ω are closely related by the analytic properties of ε. In particular, because ε is an analytic function on the real-ω axis, adding a small imaginary part to ω as we are doing here does not change ε by much. The losses are small for all of the computed k points. The change was only significant for the imaginary ε because the imaginary ε was small to begin with.