Propagation loss

docs/_toc.yml

@@ -37,6 +37,7 @@ parts:
     - file: photonics/examples/coupled_mode_theory.py
     - file: photonics/examples/depletion_waveguide.py
     - file: photonics/examples/refinement.py
+    - file: photonics/examples/propagation_loss.py
     - file: electronics/examples/capacitor.py
 
   - caption: Julia Examples

docs/photonics/examples/propagation_loss.py

@@ -0,0 +1,255 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.14.5
+#   kernelspec:
+#     display_name: femwell
+#     language: python
+#     name: python3
+# ---
+
+# # Physics-informed propagation loss model
+#
+# The ability to locally refine the mesh makes FEM well-suited to problems with very different lengthscales.
+#
+# One such problem is empirically modeling the propagation loss due to sidewall roughness, for instance as performed in {cite}`Lindecrantz2014`.
+
+# + tags=["remove-stderr"]
+
+from collections import OrderedDict
+
+import numpy as np
+import shapely
+from scipy.optimize import curve_fit
+from shapely.affinity import scale
+from shapely.ops import clip_by_rect
+from skfem import Basis, ElementTriP0
+from skfem.io.meshio import from_meshio
+
+from femwell.maxwell.waveguide import compute_modes
+from femwell.mesh import mesh_from_OrderedDict
+from femwell.visualization import plot_domains
+
+# -
+
+# Assume there is some information available about TE waveguide loss as a function of wavelength and width:
+
+# +
+# Foundry-reported information
+wavelengths = (1.55, 1.55)
+widths = (0.5, 1)
+slab_heights = (0.0, 0.0)
+losses = ydata = np.array([2, 1])
+core_thickness = 0.22
+n_si = 3.45
+n_sio2 = 1.44
+
+# Model hyperparameters
+sidewall_extent = 0.01
+
+# Format training data
+xdata = []
+for wavelength, width, slab_height in zip(wavelengths, widths, slab_heights):
+    xdata.append((wavelength, width, slab_height))
+xdata = np.array(xdata)
+
+
+# -
+
+# Assuming sidewall roughness dominates the loss, we prepare the following mesh:
+
+
+def waveguide(
+    core_width,
+    slab_thickness,
+    core_thickness=core_thickness,
+    slab_width=4,
+    sidewall_extent=0.02,
+    sidewall_k=1e-4,
+    material_k=1e-5,
+):
+    core = shapely.geometry.box(-core_width / 2, 0, +core_width / 2, core_thickness)
+
+    # Core sidewalls (only keep side extensions)
+    core_sidewalls = core.buffer(sidewall_extent, resolution=8)
+    core_sidewalls = clip_by_rect(core_sidewalls, -np.inf, 0, np.inf, core_thickness)
+
+    if slab_thickness:
+        slab = shapely.geometry.box(-slab_width / 2, 0, +slab_width / 2, slab_thickness)
+        waveguide = shapely.union(core, slab)
+        clad = scale(waveguide.buffer(5, resolution=8), xfact=0.5)
+        polygons = OrderedDict(
+            slab=slab,
+            core=core,
+            core_sidewalls=core_sidewalls,
+            clad=clad,
+        )
+    else:
+        clad = scale(core.buffer(5, resolution=8), xfact=0.5)
+        polygons = OrderedDict(
+            core=core,
+            core_sidewalls=core_sidewalls,
+            clad=clad,
+        )
+    resolutions = dict(
+        core={"resolution": 0.03, "distance": 0.5},
+        core_sidewalls={"resolution": 0.005, "distance": 0.5},
+        slab={"resolution": 0.06, "distance": 0.5},
+    )
+
+    mesh = from_meshio(mesh_from_OrderedDict(polygons, resolutions, default_resolution_max=10))
+
+    basis0 = Basis(mesh, ElementTriP0())
+    epsilon = basis0.zeros(dtype=complex)
+
+    materials = {
+        "core": n_si - 1j * material_k,
+        "core_sidewalls": n_sio2 - 1j * sidewall_k,
+        "clad": n_sio2,
+    }
+
+    if slab_thickness:
+        materials["slab"] = n_si - 1j * material_k
+
+    for subdomain, n in materials.items():
+        epsilon[basis0.get_dofs(elements=subdomain)] = n**2
+
+    return mesh, basis0, epsilon
+
+
+# +
+mesh, basis0, epsilon = waveguide(
+    core_width=0.5,
+    slab_thickness=0.0,
+    core_thickness=0.22,
+)
+
+plot_domains(mesh)
+basis0.plot(epsilon.real, colorbar=True).show()
+basis0.plot(epsilon.imag, colorbar=True).show()
+
+
+# -
+
+# Now that we have a simulation, we can compute TE0 modes, and fit the hyperparameters `sidewall_extent` and `sidewall_index` to get a better model for loss as a function of waveguide geometry:
+
+
+def compute_propagation_loss(
+    wavelength,
+    core_width,
+    slab_thickness,
+    core_thickness=core_thickness,
+    slab_width=4,
+    sidewall_extent=sidewall_extent,
+    sidewall_k=1e-4,
+    material_k=1e-5,
+):
+    mesh, basis0, epsilon = waveguide(
+        core_width=core_width,
+        slab_thickness=slab_thickness,
+        core_thickness=core_thickness,
+        slab_width=slab_width,
+        sidewall_extent=sidewall_extent,
+        sidewall_k=sidewall_k,
+        material_k=material_k,
+    )
+
+    modes = compute_modes(basis0, epsilon, wavelength=wavelength, num_modes=1, order=2)
+
+    keff = modes[0].n_eff.imag
+    wavelength_m = wavelength * 1e-6  # convert to m
+    alpha = -4 * np.pi * keff / wavelength_m
+    return 10 * np.log10(np.exp(1)) * alpha * 1e-2  # convert to cm
+
+
+for wavelength, core_width, slab_thickness, loss in zip(wavelengths, widths, slab_heights, losses):
+    predicted_loss = compute_propagation_loss(
+        wavelength=wavelength,
+        core_width=core_width,
+        slab_thickness=slab_thickness,
+        core_thickness=core_thickness,
+        slab_width=4,
+        sidewall_extent=sidewall_extent,
+        sidewall_k=3e-4,
+        material_k=2.5e-6,
+    )
+
+    print(wavelength, core_width, slab_thickness, predicted_loss, loss)
+
+
+# Pretty close, refine through optimization:
+
+
+def objective_vector(xdata, sidewall_k, material_k):
+    losses_obj = []
+    for wavelength, width, slab_height in xdata:
+        losses_obj.append(
+            compute_propagation_loss(
+                wavelength=wavelength,
+                core_width=width,
+                slab_thickness=slab_height,
+                core_thickness=core_thickness,
+                slab_width=4,
+                sidewall_extent=sidewall_extent,
+                sidewall_k=sidewall_k,
+                material_k=material_k,
+            )
+        )
+    return losses_obj
+
+
+popt, pcov = curve_fit(objective_vector, xdata, ydata, bounds=(0, [1e-2, 1e-2]), p0=(3e-4, 1e-6))
+
+popt, pcov
+
+for wavelength, core_width, slab_thickness, loss in zip(wavelengths, widths, slab_heights, losses):
+    predicted_loss = compute_propagation_loss(
+        wavelength=wavelength,
+        core_width=core_width,
+        slab_thickness=slab_thickness,
+        core_thickness=core_thickness,
+        slab_width=4,
+        sidewall_extent=sidewall_extent,
+        sidewall_k=popt[0],
+        material_k=popt[1],
+    )
+
+    print(wavelength, core_width, slab_thickness, predicted_loss, loss)
+
+widths_plot = np.linspace(0.275, 2.0, 19)
+losses_plot_strip = []
+for width in widths_plot:
+    losses_plot_strip.append(
+        compute_propagation_loss(
+            wavelength=1.55,
+            core_width=width,
+            slab_thickness=0.0,
+            core_thickness=core_thickness,
+            slab_width=4,
+            sidewall_extent=sidewall_extent,
+            sidewall_k=popt[0],
+            material_k=popt[1],
+        )
+    )
+
+# +
+import matplotlib.pyplot as plt
+
+plt.plot(widths_plot, losses_plot_strip, label="strip model")
+plt.scatter(widths, losses, label="strip data")
+
+plt.legend()
+plt.xlabel("Core width (um)")
+plt.ylabel("Propagation loss (dB/cm)")
+# -
+
+# ## Bibliography
+#
+# ```{bibliography}
+# :style: unsrt
+# :filter: docname in docnames
+# ```

docs/references.bib

@@ -243,3 +243,13 @@ @article{Vardapetyan2003
     doi           = {10.1016/s0045-7825(02)00539-x},
     url           = {https://doi.org/10.1016/s0045-7825(02)00539-x}
 }
+@ARTICLE{Lindecrantz2014,
+  author={Lindecrantz, Susan M. and Hellesø, Olav Gaute},
+  journal={IEEE Photonics Technology Letters}, 
+  title={Estimation of Propagation Losses for Narrow Strip and Rib Waveguides}, 
+  year={2014},
+  volume={26},
+  number={18},
+  pages={1836-1839},
+  doi={10.1109/LPT.2014.2337055}
+ }