General Ring Networks

Imports

import torch
import numpy as np
import matplotlib.pyplot as plt
import photontorch as pt
from tqdm.notebook import trange

torch.manual_seed(42)
np.random.seed(33)

Add Drop Filter

Let’s try to recreate the add drop filter in 02_add_drop.ipynb, but this time using the RingNetwork class:

env = pt.Environment(
    dt = 1e-14,
    t_end=2000e-14,
    wl=1e-6*np.linspace(1.5, 1.6, 1000),
)
env

key	value	description
name	env	name of the environment
t	[0.000e+00, 1.000e-14, ..., 1.998e-11]	[s] full 1D time array.
t0	0.000e+00	[s] starting time of the simulation.
t1	1.999e-11	[s] ending time of the simulation.
num_t	1999	number of timesteps in the simulation.
dt	1.000e-14	[s] timestep of the simulation
samplerate	1.000e+14	[1/s] samplerate of the simulation.
bitrate	None	[1/s] bitrate of the signal.
bitlength	None	[s] bitlength of the signal.
wl	[1.500e-06, 1.500e-06, ..., 1.600e-06]	[m] full 1D wavelength array.
wl0	1.500e-06	[m] start of wavelength range.
wl1	1.600e-06	[m] end of wavelength range.
num_wl	1000	number of independent wavelengths in the simulation
dwl	1.001e-10	[m] wavelength step sizebetween wl0 and wl1.
f	[1.999e+14, 1.998e+14, ..., 1.874e+14]	[1/s] full 1D frequency array.
f0	1.999e+14	[1/s] start of frequency range.
f1	1.874e+14	[1/s] end of frequency range.
num_f	1000	number of independent frequencies in the simulation
df	-1.334e+10	[1/s] frequency step between f0 and f1.
c	2.998e+08	[m/s] speed of light used during simulations.
freqdomain	False	only do frequency domain calculations.
grad	False	track gradients during the simulation

nw = pt.RingNetwork(
    N=2,
    capacity = 3,
    wg_factory = lambda: pt.Waveguide(length=50e-6/2, neff=2.86),
    mzi_factory = lambda: pt.DirectionalCoupler(coupling=0.3),
).terminate([pt.Source("term_in"), pt.Detector("term_pass"), pt.Detector("term_drop"), pt.Detector("term_add")])
with env.copy(wl=env.wavelength.mean()):
    detected = nw(source=1)
    nw.plot(detected)
    plt.show()

with env.copy(freqdomain=True):
    detected = nw(source=1)
    nw.plot(detected)
    plt.show()

Of course you can create much bigger and complicated ring networks, but that’s for another time.