# Copyright © Simphony Project Contributors
# Licensed under the terms of the MIT License
# (see simphony/__init__.py for details)
"""SiEPIC models compatible with SAX."""
import importlib.resources
import re
from functools import lru_cache
from pathlib import Path
from typing import List, Literal, Union
import jax.numpy as jnp
import numpy as np
import pandas as pd
import sax
from jax import Array
from jax.typing import ArrayLike
from scipy.constants import c as SPEED_OF_LIGHT
from tabulate import tabulate
import simphony.libraries
from simphony.plugins.lumerical import df_to_sdict, load_sparams
from simphony.utils import freq2wl, resample, wl2freq
SOURCE_DATA_PATH = "siepic/source_data"
def _resolve_source_filepath(filename: str) -> Path:
"""Gets the absolute path to the source data files relative to ``source_data/``.
Parameters
----------
filename : str
The name of the file to be found.
Returns
-------
filepath : str
The absolute path to the file.
"""
filepath = Path(SOURCE_DATA_PATH) / filename
try: # python >= 3.9
return importlib.resources.files(simphony.libraries) / filepath
except AttributeError: # fall back to method deprecated in 3.11.
ctx = importlib.resources.path(simphony, "libraries")
with ctx as path:
return path / filepath
@lru_cache()
def _load_txt_cached(path: Union[Path, str]) -> np.ndarray:
"""Loads a text file from the source_data directory and caches it.
Parameters
----------
filename : str
The name of the file to be loaded.
Returns
-------
content : str
The contents of the file.
"""
return np.loadtxt(path)
def _create_sdict_from_df(wl: Union[float, ArrayLike], df: pd.DataFrame) -> sax.SDict:
"""Create an s-dictionary from a dataframe of s-parameters.
Parameters
----------
wl : float or ArrayLike
Wavelengths to interpolate the s-parameters to (in microns).
df : pandas.DataFrame
A dataframe of s-parameters. Must have columns 'port_in', 'port_out',
'freq' (in Hz), 'mag', and 'phase'.
"""
wl_m = jnp.array(wl).reshape(-1) * 1e-6 # meters
f, s = df_to_sdict(df)
new_s = resample(wl2freq(wl_m), f, s)
return new_s
def _generate_parameter_sets(
pattern: str,
path: Union[str, Path],
ext: str = "sparam",
columns: List[str] = [],
) -> pd.DataFrame:
"""Generate a dataframe of all valid parameter sets by parsing the
filenames of the data files in the source directory.
Parameters
----------
pattern : str
Regex pattern with named fields to match filenames against.
path : str or Path
Path to the source directory.
ext : str, optional
File extension to match (default 'sparam').
columns : list of str
List of columns to convert to numeric types. Should be a subset of the
named fields in the regex pattern.
Returns
-------
df : pandas.DataFrame
Dataframe of valid parameter sets.
"""
path = _resolve_source_filepath(path)
params = []
for file in [str(p.name) for p in path.glob(f"*.{ext}")]:
m = re.match(pattern, file)
if m:
params.append(m.groupdict())
df = pd.DataFrame(params)
for col in columns:
df[col] = pd.to_numeric(df[col], errors="coerce")
# Find out which columns have the most unique items
unique_counts = df.nunique()
# Sort dataframe by fewest unique items in column to most unique items
sorted_values = unique_counts.sort_values().index.tolist()
df = df.sort_values(by=sorted_values, ignore_index=True)
# Rearrange columns of dataframe to go from fewest unique items to most
sorted_df = df[sorted_values]
return sorted_df
def _generate_parameter_table_rst(df) -> str:
"""Generate an rst (text) table of valid parameter combinations.
Useful for adding to the docstring of various models.
Examples
--------
>>> df = _generate_parameter_sets()
>>> print(_generate_parameter_table_rst(df))
"""
df.reset_index(drop=True, inplace=True)
return tabulate(df.values, df.columns, tablefmt="rst")
def _stringify_float(val: float, max_precision: int = 1) -> str:
"""Convert a float to a string, with a maximum precision."""
if val == int(val):
return str(int(val))
else:
return f"{val:.{max_precision}f}"
[docs]def bidirectional_coupler(
wl: Union[float, ArrayLike] = 1.55,
thickness: float = 220,
width: float = 500,
) -> sax.SDict:
"""SiEPIC EBeam PDK bidirectional coupler model.
A bidirectional coupler optimized for TE polarized light at 1550nm.
The bidirectional coupler has 4 ports, labeled as pictured. Its efficiently
splits light that is input from one port into the two outputs on the opposite
side (with a corresponding pi/2 phase shift). Additionally, it efficiently
interferes lights from two adjacent inputs, efficiently splitting the
interfered signal between the two ports on the opposing side.
.. image:: /_static/images/ebeam_bdc_te1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
thickness : float, optional
Waveguide thickness, in nanometers (default 220). Valid values
are 210, 220, or 230 nanometers.
width : float, optional
Waveguide width, in nanometers (default 500). Valid values are
480, 500, or 520 nanometers.
Notes
-----
See also the PDK documentation:
https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx
"""
if thickness not in [210.0, 220.0, 230.0]:
raise ValueError("'thickness' must be one of 210, 220, or 230")
thickness = str(int(thickness))
if width not in [480.0, 500.0, 520.0]:
raise ValueError("'width' must be one of 480, 500, or 520")
width = str(int(width))
datafile = f"bdc_TE_source/bdc_Thickness ={thickness} width={width}.sparam"
file = _resolve_source_filepath(datafile)
header, data = load_sparams(file)
return _create_sdict_from_df(wl, data)
[docs]def directional_coupler(
wl: Union[float, ArrayLike] = 1.55,
gap: float = 200,
coupling_length: float = 10.0,
) -> sax.SDict:
"""A directional coupler optimized for TE polarized light at 1550nm.
The directional coupler has 4 ports, labeled as pictured. Its efficiently
splits light that is input from one port into the two outputs on the opposite
side (with a corresponding pi/2 phase shift). Additionally, it efficiently
interferes lights from two adjacent inputs, efficiently splitting the
interfered signal between the two ports on the opposing side.
.. image:: /_static/images/ebeam_bdc_te1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
gap : float, optional
Coupling gap distance, in nanometers (default 200).
coupling_length : float, optional
Length of coupler, in microns (default 10).
Notes
-----
Sorted matrix of valid parameter combinations for directional couplers:
===== =================
gap coupling_length
===== =================
200 0
200 2.5
200 5
200 7.5
200 10
200 12.5
200 15
200 17.5
200 20
200 22.5
200 25
200 27.5
200 30
200 32.5
200 35
200 37.5
200 40
200 42.5
200 45
200 47.5
===== =================
"""
# df = self._generate_parameter_sets()
# if not ((df["gap"] == gap) & (df["coupling_length"] == coupling_length)).any():
# raise ValueError(
# "Invalid parameter set, see the documentation for valid parameter sets"
# )
_datafile = f"ebeam_dc_te1550/dc_gap={int(gap)}nm_Lc={_stringify_float(coupling_length)}um.sparam"
file = _resolve_source_filepath(_datafile)
header, data = load_sparams(file)
return _create_sdict_from_df(wl, data)
def _generate_parameter_sets_dc() -> pd.DataFrame:
"""Generate a dataframe of all valid parameter sets by parsing the
filenames of the data files in the source directory."""
return _generate_parameter_sets(
r"dc_gap=(?P<gap>\d+)nm_Lc=(?P<coupling_length>\d+)um.sparam",
"ebeam_dc_te1550",
ext="sparam",
columns=["gap", "coupling_length"],
)
[docs]def half_ring(
wl: Union[float, ArrayLike] = 1.55,
pol: Literal["te", "tm"] = "te",
gap: float = 50,
radius: float = 5,
width: float = 500,
thickness: float = 220,
coupling_length: float = 0,
) -> sax.SDict:
"""A half-ring resonator optimized for TE polarized light at 1550nm.
The halfring has 4 ports, labeled as pictured.
.. image:: /_static/images/halfring.png
:alt: halfring.png
Parameters
----------
pol : str, optional
Polarization of the halfring. Must be either 'te' (default) or 'tm'.
gap : float, optional
Coupling distance between ring and straight waveguide in nanometers
(default 50).
radius : float, optional
Ring radius in microns (default 5).
width : float, optional
Waveguide width in nanometers (default 500).
thickness : float, optional
Waveguide thickness in nanometers (default 220).
coupling_length : float, optional
Length of the straight segment of the directional coupling edge, turns
ring into a racetrack resonator, in microns (default 0).
Notes
-----
Sorted matrix of valid parameter combinations for half rings:
===== ================= ======= =========== ======== =====
pol coupling_length width thickness radius gap
===== ================= ======= =========== ======== =====
te 0 480 210 3 70
te 0 480 210 3 80
te 0 480 210 3 100
te 0 480 210 3 120
te 0 480 210 5 70
te 0 480 210 5 80
te 0 480 210 5 120
te 0 480 210 10 120
te 0 480 210 10 170
te 0 480 230 3 70
te 0 480 230 3 80
te 0 480 230 3 100
te 0 480 230 3 120
te 0 480 230 5 70
te 0 480 230 5 80
te 0 480 230 5 120
te 0 480 230 10 120
te 0 480 230 10 170
te 0 500 220 3 50
te 0 500 220 3 60
te 0 500 220 3 80
te 0 500 220 3 100
te 0 500 220 5 50
te 0 500 220 5 60
te 0 500 220 5 100
te 0 500 220 10 100
te 0 500 220 10 150
te 0 500 220 18 200
te 0 520 210 3 30
te 0 520 210 3 40
te 0 520 210 3 60
te 0 520 210 3 80
te 0 520 210 5 30
te 0 520 210 5 40
te 0 520 210 5 80
te 0 520 210 10 80
te 0 520 210 10 130
te 0 520 230 3 30
te 0 520 230 3 40
te 0 520 230 3 60
te 0 520 230 3 80
te 0 520 230 5 30
te 0 520 230 5 40
te 0 520 230 5 80
te 0 520 230 10 80
te 0 520 230 10 130
te 4 500 220 10 200
tm 0 480 210 5 320
tm 0 480 230 5 320
tm 0 500 220 5 300
tm 0 520 210 5 280
tm 0 520 230 5 280
===== ================= ======= =========== ======== =====
"""
if pol not in ["te", "tm"]:
raise ValueError("'pol' must be one of 'te' or 'tm'")
# df = self._generate_parameter_sets()
# if not (
# (df["pol"] == pol)
# & (df["gap"] == gap)
# & (df["radius"] == radius)
# & (df["width"] == width)
# & (df["thickness"] == thickness)
# & (df["coupling_length"] == coupling_length)
# ).any():
# raise ValueError(
# "Invalid parameter set, see the documentation for valid parameter sets"
# )
# # (df == a).all(1).any()
_datafile = f"ebeam_dc_halfring_straight/{pol}_ebeam_dc_halfring_straight_gap={int(gap)}nm_radius={int(radius)}um_width={int(width)}nm_thickness={int(thickness)}nm_CoupleLength={int(coupling_length)}um.dat"
file = _resolve_source_filepath(_datafile)
header, data = load_sparams(file)
return _create_sdict_from_df(wl, data)
def _generate_parameter_sets_half_ring() -> pd.DataFrame:
"""Generate a dataframe of all valid parameter sets by parsing the
filenames of the data files in the source directory."""
return _generate_parameter_sets(
r"(?P<pol>[a-z]+)_ebeam_dc_halfring_straight_gap=(?P<gap>\d+)nm_radius=(?P<radius>\d+)um_width=(?P<width>\d+)nm_thickness=(?P<thickness>\d+)nm_CoupleLength=(?P<coupling_length>\d+)um.dat"
"ebeam_dc_halfring_straight",
ext="dat",
columns=["gap", "radius", "width", "thickness", "coupling_length"],
)
[docs]def taper(
wl: Union[float, ArrayLike] = 1.55,
w1: float = 0.5,
w2: float = 1.0,
length: float = 10.0,
) -> sax.SDict:
"""A taper component that adiabatically transitions between two waveguide
widths.
This taper is simulated for TE operation at 1550 nanometers.
.. image:: /_static/images/ebeam_taper_te1550.png
:alt: ebeam_taper_te1550.png
Parameters
----------
w1 : float, optional
Width of the input waveguide in microns (default 0.5).
w2 : float, optional
Width of the output waveguide in microns (default 1).
length : float, optional
Length of the taper in microns (default 10).
Notes
-----
Sorted matrix of valid parameter combinations for adiabatic tapers:
==== ==== ========
w1 w2 length
==== ==== ========
0.4 1 1
0.4 1 2
0.4 1 3
0.4 1 4
0.4 1 5
0.4 1 6
0.4 1 7
0.4 1 8
0.4 1 9
0.4 1 10
0.4 1 11
0.4 1 12
0.4 1 13
0.4 1 14
0.4 1 15
0.4 1 16
0.4 1 17
0.4 1 18
0.4 1 19
0.4 1 20
0.4 2 1
0.4 2 2
0.4 2 3
0.4 2 4
0.4 2 5
0.4 2 6
0.4 2 7
0.4 2 8
0.4 2 9
0.4 2 10
0.4 2 11
0.4 2 12
0.4 2 13
0.4 2 14
0.4 2 15
0.4 2 16
0.4 2 17
0.4 2 18
0.4 2 19
0.4 2 20
0.4 3 1
0.4 3 2
0.4 3 3
0.4 3 4
0.4 3 5
0.4 3 6
0.4 3 7
0.4 3 8
0.4 3 9
0.4 3 10
0.4 3 11
0.4 3 12
0.4 3 13
0.4 3 14
0.4 3 15
0.4 3 16
0.4 3 17
0.4 3 18
0.4 3 19
0.4 3 20
0.5 1 1
0.5 1 2
0.5 1 3
0.5 1 4
0.5 1 5
0.5 1 6
0.5 1 7
0.5 1 8
0.5 1 9
0.5 1 10
0.5 1 11
0.5 1 12
0.5 1 13
0.5 1 14
0.5 1 15
0.5 1 16
0.5 1 17
0.5 1 18
0.5 1 19
0.5 1 20
0.5 2 1
0.5 2 2
0.5 2 3
0.5 2 4
0.5 2 5
0.5 2 6
0.5 2 7
0.5 2 8
0.5 2 9
0.5 2 10
0.5 2 11
0.5 2 12
0.5 2 13
0.5 2 14
0.5 2 15
0.5 2 16
0.5 2 17
0.5 2 18
0.5 2 19
0.5 2 20
0.5 3 1
0.5 3 2
0.5 3 3
0.5 3 4
0.5 3 5
0.5 3 6
0.5 3 7
0.5 3 8
0.5 3 9
0.5 3 10
0.5 3 11
0.5 3 12
0.5 3 13
0.5 3 14
0.5 3 15
0.5 3 16
0.5 3 17
0.5 3 18
0.5 3 19
0.5 3 20
0.6 1 1
0.6 1 2
0.6 1 3
0.6 1 4
0.6 1 5
0.6 1 6
0.6 1 7
0.6 1 8
0.6 1 9
0.6 1 10
0.6 1 11
0.6 1 12
0.6 1 13
0.6 1 14
0.6 1 15
0.6 1 16
0.6 1 17
0.6 1 18
0.6 1 19
0.6 1 20
0.6 2 1
0.6 2 2
0.6 2 3
0.6 2 4
0.6 2 5
0.6 2 6
0.6 2 7
0.6 2 8
0.6 2 9
0.6 2 10
0.6 2 11
0.6 2 12
0.6 2 13
0.6 2 14
0.6 2 15
0.6 2 16
0.6 2 17
0.6 2 18
0.6 2 19
0.6 2 20
0.6 3 1
0.6 3 2
0.6 3 3
0.6 3 4
0.6 3 5
0.6 3 6
0.6 3 7
0.6 3 8
0.6 3 9
0.6 3 10
0.6 3 11
0.6 3 12
0.6 3 13
0.6 3 14
0.6 3 15
0.6 3 16
0.6 3 17
0.6 3 18
0.6 3 19
0.6 3 20
==== ==== ========
"""
# df = self._generate_parameter_sets()
# if not ((df["w1"] == w1) & (df["w2"] == w2) & (df["length"] == length)).any():
# raise ValueError(
# "Invalid parameter set, see the documentation for valid parameter sets"
# )
_datafile = (
f"ebeam_taper_te1550/w1={w1:.1f}um_w2={int(w2)}um_length={int(length)}um.dat"
)
path = _resolve_source_filepath(_datafile)
arr = _load_txt_cached(path)
f = arr[:, 0]
wl_samp = freq2wl(f) # meters
wl_des = jnp.asarray(wl).reshape(-1) * 1e-6 # meters
s11 = jnp.interp(wl_des, wl_samp, arr[:, 1] * jnp.exp(1j * arr[:, 2]))
s12 = jnp.interp(wl_des, wl_samp, arr[:, 3] * jnp.exp(1j * arr[:, 4]))
s21 = jnp.interp(wl_des, wl_samp, arr[:, 5] * jnp.exp(1j * arr[:, 6]))
s22 = jnp.interp(wl_des, wl_samp, arr[:, 7] * jnp.exp(1j * arr[:, 8]))
sdict = {
("o0", "o0"): s11,
("o0", "o1"): s12,
("o1", "o0"): s21,
("o1", "o1"): s22,
}
return sdict
def _generate_parameter_sets_taper() -> pd.DataFrame:
"""Generate a dataframe of all valid parameter sets by parsing the
filenames of the data files in the source directory."""
return _generate_parameter_sets(
r"w1=(?P<w1>\d+\.\d+)um_w2=(?P<w2>\d+)um_length=(?P<length>\d+)um.dat"
"ebeam_taper_te1550",
ext="dat",
columns=["w1", "w2", "length"],
)
[docs]def terminator(
wl: Union[float, ArrayLike] = 1.55,
pol: Literal["te", "tm"] = "te",
) -> sax.SDict:
"""A terminator component that dissipates light into free space optimized
for TE polarized light at 1550 nanometers.
The terminator dissipates excess light into free space. If you have a path
where the light doesn't need to be measured but you don't want it reflecting
back into the circuit, you can use a terminator to release it from the circuit.
.. image:: /_static/images/ebeam_terminator_te1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
pol : str, optional
Polarization of the grating coupler. Must be either 'te' (default) or
'tm'.
"""
if pol not in ["te", "tm"]:
raise ValueError("'pol' must be one of 'te' or 'tm'")
if pol == "te":
_datafile = "ebeam_terminator_te1550/nanotaper_w1=500,w2=60,L=10_TE.sparam"
else:
_datafile = "ebeam_terminator_tm1550/nanotaper_w1=500,w2=60,L=10_TM.sparam"
file = _resolve_source_filepath(_datafile)
header, data = load_sparams(file)
return _create_sdict_from_df(wl, data)
[docs]def grating_coupler(
wl: Union[float, Array] = 1.55,
pol: Literal["te", "tm"] = "te",
thickness: float = 220.0,
dwidth: float = 0,
) -> sax.SDict:
"""SiEPIC EBeam PDK grating coupler optimized for TE polarizations at
1550nm.
The grating coupler efficiently couples light from a fiber array positioned
above the chip into the circuit. For the TE mode, the angle is -25 degrees
[needs citation].
.. image:: /_static/images/ebeam_gc_te1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
wl : float or Array
The wavelengths to evaluate at in microns.
pol : {"te", "tm"}
Polarization of the input/output modes.
thickness : {210.0, 220.0, 230.0}
Thickness of the grating coupler silicon in nm. Useful for simulating
manufacturing variability.
dwidth : {-20.0, 0.0, 20.0}
Change in width from nominal of the gratings. Representative of
manufacturing variability. Must be one of -20, 0, or 20.
Raises
------
ValueError
If `pol` is not 'te' or 'tm'.
ValueError
If `thickness` is not one of 210, 220, or 230.
ValueError
If `dwidth` is not one of -20, 0, or 20.
Notes
-----
See also the PDK documentation:
https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx
"""
if pol not in ["te", "tm"]:
raise ValueError("'pol' must be either 'te' or 'tm'")
pol = pol.upper()
if thickness not in [210.0, 220.0, 230.0]:
raise ValueError("'thickness' must be one of 210.0, 220.0, or 230.0")
thickness = str(int(thickness))
if dwidth not in [-20.0, 0.0, 20.0]:
raise ValueError("'dwidth' must be one of -20, 0, or 20")
dwidth = str(int(dwidth))
_datafile = f"gc_source/GC_{pol}1550_thickness={thickness} deltaw={dwidth}.txt"
path = _resolve_source_filepath(_datafile)
arr = _load_txt_cached(path)
f = arr[:, 0]
wl_samp = freq2wl(f) # meters
wl_des = jnp.asarray(wl).reshape(-1) * 1e-6 # meters
s11 = jnp.interp(wl_des, wl_samp, arr[:, 1] * jnp.exp(1j * arr[:, 2]))
s12 = jnp.interp(wl_des, wl_samp, arr[:, 3] * jnp.exp(1j * arr[:, 4]))
s21 = jnp.interp(wl_des, wl_samp, arr[:, 5] * jnp.exp(1j * arr[:, 6]))
s22 = jnp.interp(wl_des, wl_samp, arr[:, 7] * jnp.exp(1j * arr[:, 8]))
sdict = {
("o0", "o0"): s11,
("o0", "o1"): s12,
("o1", "o0"): s21,
("o1", "o1"): s22,
}
return sdict
[docs]def waveguide(
wl: Union[float, Array] = 1.55,
pol: Literal["te", "tm"] = "te",
length: float = 0.0,
width: float = 500.0,
height: float = 220.0,
loss: float = 0.0,
) -> sax.SDict:
"""Model for an waveguide optimized for TE polarized light at 1550
nanometers.
A waveguide easily connects other optical components within a circuit.
.. image:: /_static/images/ebeam_wg_integral_1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
pol : str, optional
Polarization of the grating coupler. Must be either 'te' (default) or
'tm'.
length : float, optional
Waveguide length in microns (default 0).
width : float, optional
Waveguide width in nanometers (default 500).
height : float, optional
Waveguide height in nanometers (default 220).
loss : float, optional
Loss of the waveguide in dB/cm (default 0).
sigma_ne : float, optional
Standard deviation of the effective index for monte carlo simulations
(default 0.05).
sigma_ng : float, optional
Standard deviation of the group velocity for monte carlo simulations
(default 0.05).
sigma_nd : float, optional
Standard deviation of the group dispersion for monte carlo simulations
(default 0.0001).
Notes
-----
The `sigma_` values in the parameters are used for monte carlo simulations.
Sorted matrix of valid parameter combinations for waveguides:
======== =======
height width
======== =======
210 400
210 420
210 440
210 460
210 480
210 500
210 520
210 540
210 560
210 580
210 600
210 640
210 680
210 720
210 760
210 800
210 840
210 880
210 920
210 960
210 1000
210 1040
210 1080
210 1120
210 1160
210 1200
210 1240
210 1280
210 1320
210 1360
210 1400
210 1500
210 1600
210 1700
210 1800
210 1900
210 2000
210 2100
210 2200
210 2300
210 2400
210 2500
210 2600
210 2700
210 2800
210 2900
210 3000
210 3100
210 3200
210 3300
210 3400
210 3500
220 400
220 420
220 440
220 460
220 480
220 500
220 520
220 540
220 560
220 580
220 600
220 640
220 680
220 720
220 760
220 800
220 840
220 880
220 920
220 960
220 1000
220 1040
220 1080
220 1120
220 1160
220 1200
220 1240
220 1280
220 1320
220 1360
220 1400
220 1500
220 1600
220 1700
220 1800
220 1900
220 2000
220 2100
220 2200
220 2300
220 2400
220 2500
220 2600
220 2700
220 2800
220 2900
220 3000
220 3100
220 3200
220 3300
220 3400
220 3500
230 400
230 420
230 440
230 460
230 480
230 500
230 520
230 540
230 560
230 580
230 600
230 640
230 680
230 720
230 760
230 800
230 840
230 880
230 920
230 960
230 1000
230 1040
230 1080
230 1120
230 1160
230 1200
230 1240
230 1280
230 1320
230 1360
230 1400
230 1500
230 1600
230 1700
230 1800
230 1900
230 2000
230 2100
230 2200
230 2300
230 2400
230 2500
230 2600
230 2700
230 2800
230 2900
230 3000
230 3100
230 3200
230 3300
230 3400
230 3500
======== =======
"""
if pol not in ["te", "tm"]:
raise ValueError("Invalid polarization, must be either 'te' or 'tm'")
width, height = int(width), int(height)
# df = self._generate_parameter_sets()
# if not ((df["width"] == width) & (df["height"] == height)).any():
# raise ValueError(
# "Invalid parameter set, see the documentation for valid parameter sets"
# )
_datafile = f"wg_integral_source/WaveGuideTETMStrip,w={width},h={height}.txt"
# Load data file, extract coefficients
path = _resolve_source_filepath(_datafile)
arr = _load_txt_cached(path)
if pol == "te":
lam0, ne, _, ng, _, nd, _ = arr
else: # tm
lam0, _, ne, _, ng, _, nd = arr
wl_m = jnp.asarray(wl).reshape(-1) * 1e-6 # convert microns to meters
freqs = wl2freq(wl_m) # convert wavelengths to freqs
length_m = length * 1e-6 # convert microns to meters
loss = loss * 100 # convert loss from dB/cm to dB/m
alpha = loss / (20 * jnp.log10(jnp.exp(1))) # convert loss to m^-1
omega = 2 * jnp.pi * jnp.asarray(freqs) # get angular freqs from freqs
omega0 = (2 * jnp.pi * SPEED_OF_LIGHT) / lam0 # center freqs (angular)
# calculation of K
K = (
2 * jnp.pi * ne / lam0
+ (ng / SPEED_OF_LIGHT) * (omega - omega0)
- (nd * lam0**2 / (4 * jnp.pi * SPEED_OF_LIGHT)) * ((omega - omega0) ** 2)
)
sdict = {
("o0", "o0"): jnp.zeros(wl_m.shape, dtype=np.complex128),
("o0", "o1"): jnp.exp(-alpha * length_m + (1j * K * length_m)),
("o1", "o0"): jnp.exp(-alpha * length_m + (1j * K * length_m)),
("o1", "o1"): jnp.zeros(wl_m.shape, dtype=np.complex128),
}
return sdict
def _generate_parameter_sets_waveguide() -> pd.DataFrame:
"""Generate a dataframe of all valid parameter sets by parsing the
filenames of the data files in the source directory."""
return _generate_parameter_sets(
r"WaveGuideTETMStrip,w=(?P<width>\d+),h=(?P<height>\d+).txt",
"wg_integral_source",
ext="txt",
columns=["width", "height"],
)
[docs]def y_branch(
wl: Union[float, Array] = 1.55,
pol: Literal["te", "tm"] = "te",
thickness: float = 220.0,
width: float = 500.0,
) -> sax.SDict:
"""SiEPIC EBeam PDK Y-branch model.
A y-branch efficiently splits the input 50/50 between the two outputs.
It can also be used as a combiner if used in the opposite direction,
combining and interfering the light from two inputs into the one output.
.. image:: /_static/images/ebeam_y_1550.png
:alt: ebeam_bdc_te1550.png
Parameters
----------
pol : str, optional
Polarization of the y-branch. Must be either 'te' (default) or 'tm'.
thickness : float, optional
Waveguide thickness, in nanometers (default 220). Valid values
are 210, 220, or 230 nanometers. Useful for simulating manufacturing
variability.
width : float, optional
Waveguide width, in nanometers (default 500 nanometers). Valid values
are 480, 500, or 520 nanometers.
Notes
-----
See also the PDK documentation:
https://github.com/SiEPIC/SiEPIC_EBeam_PDK/blob/master/Documentation/SiEPIC_EBeam_PDK%20-%20Components%20with%20Models.docx
"""
if pol not in ["te", "tm"]:
raise ValueError("'pol' must be one of 'te' or 'tm'")
if thickness not in [210.0, 220.0, 230.0]:
raise ValueError("'thickness' must be one of 210, 220, or 230")
thickness = str(int(thickness))
if width not in [480.0, 500.0, 520.0]:
raise ValueError("'width' must be one of 480, 500, or 520")
width = str(int(width))
_datafile = f"y_branch_source/Ybranch_Thickness ={thickness} width={width}.sparam"
file = _resolve_source_filepath(_datafile)
header, data = load_sparams(file)
_POL_MAPPING = {"te": 1, "tm": 2}
MODE_ID = _POL_MAPPING[pol]
data = data[data.mode_out == MODE_ID]
return _create_sdict_from_df(wl, data)
