Wavelength Sweep
This recipe demonstrates a full visible-range wavelength sweep and how to analyze the spectral data.
Interactive Blackbody Spectrum Viewer
Adjust the color temperature to see how the blackbody spectrum changes. Enable standard illuminant overlays for comparison.
CCT:5500 K
λmax:527 nm
Visible Power:71.1%
Approx. Color
Basic sweep
The simplest wavelength sweep uses the "sweep" mode in the source config:
python
from compass.runners.single_run import SingleRunner
config = {
"pixel": {
"pitch": 1.0,
"unit_cell": [2, 2],
"bayer_map": [["R", "G"], ["G", "B"]],
"layers": {
"microlens": {"enabled": True, "height": 0.6, "radius_x": 0.48, "radius_y": 0.48},
"planarization": {"thickness": 0.3, "material": "sio2"},
"color_filter": {
"thickness": 0.6,
"materials": {"R": "cf_red", "G": "cf_green", "B": "cf_blue"},
"grid": {"enabled": True, "width": 0.05, "material": "tungsten"},
},
"barl": {"layers": [
{"thickness": 0.010, "material": "sio2"},
{"thickness": 0.025, "material": "hfo2"},
{"thickness": 0.015, "material": "sio2"},
{"thickness": 0.030, "material": "si3n4"},
]},
"silicon": {
"thickness": 3.0, "material": "silicon",
"photodiode": {"position": [0, 0, 0.5], "size": [0.7, 0.7, 2.0]},
"dti": {"enabled": True, "width": 0.1, "material": "sio2"},
},
},
},
"solver": {
"name": "torcwa", "type": "rcwa",
"params": {"fourier_order": [9, 9]},
"stability": {"precision_strategy": "mixed", "fourier_factorization": "li_inverse"},
},
"source": {
"wavelength": {
"mode": "sweep",
"sweep": {"start": 0.38, "stop": 0.78, "step": 0.01},
},
"polarization": "unpolarized",
},
"compute": {"backend": "auto"},
}
result = SingleRunner.run(config)
print(f"Sweep: {len(result.wavelengths)} wavelengths from "
f"{result.wavelengths[0]*1000:.0f} to {result.wavelengths[-1]*1000:.0f} nm")Full visible spectrum plot
python
import matplotlib.pyplot as plt
import numpy as np
from compass.visualization.qe_plot import plot_qe_spectrum
fig, ax = plt.subplots(figsize=(10, 6))
plot_qe_spectrum(result, ax=ax)
# Add wavelength color bar on x-axis for reference
wl_nm = result.wavelengths * 1000
for i in range(len(wl_nm) - 1):
color = wavelength_to_rgb(wl_nm[i]) # You'd need a helper for this
ax.axvspan(wl_nm[i], wl_nm[i+1], alpha=0.03, color=color)
ax.set_title("Full Visible Spectrum QE")
plt.tight_layout()
plt.savefig("full_spectrum_qe.png", dpi=200)Wavelength, Silicon Absorption & Pixel Response
Drag the marker along the spectrum to explore how wavelength affects silicon absorption depth, optical properties, and pixel performance.
Wavelength Selector
λ = 550 nm (Green)
Visible
Silicon Absorption Profile
Absorption depth (1/α): 1.56 um
Optical Properties at λ = 550 nm
Refractive index n(λ)
4.080
Extinction coeff. k(λ)
2.800e-2
Absorption depth 1/α
1.56 um
Absorption coeff. α
6,397 cm-1
Typical BSI QE
70%
Color region
Green
α = 4πk / λ | Absorption depth = 1/α = λ / (4πk)Reflectance, transmittance, absorption
Plot the energy balance components alongside QE:
python
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
# QE
plot_qe_spectrum(result, ax=ax1)
ax1.set_xlabel("")
# R, T, A
wl_nm = result.wavelengths * 1000
if result.reflection is not None:
ax2.plot(wl_nm, result.reflection, label="Reflection", color="tab:blue")
if result.transmission is not None:
ax2.plot(wl_nm, result.transmission, label="Transmission", color="tab:orange")
if result.absorption is not None:
ax2.plot(wl_nm, result.absorption, label="Absorption", color="tab:red")
ax2.set_xlabel("Wavelength (nm)")
ax2.set_ylabel("Fraction")
ax2.set_title("Energy Balance Components")
ax2.legend()
ax2.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("qe_and_energy.png", dpi=150)Fine-resolution sweep
For resolving thin-film interference fringes, use a finer step:
python
config_fine = config.copy()
config_fine["source"] = {
"wavelength": {
"mode": "sweep",
"sweep": {"start": 0.50, "stop": 0.60, "step": 0.002}, # 2 nm step
},
"polarization": "unpolarized",
}
result_fine = SingleRunner.run(config_fine)The 2 nm step resolves the Fabry-Perot fringes from the silicon cavity (fringe spacing ~12 nm for 3 um Si at 550 nm).
Wavelength list mode
For specific wavelengths (e.g., laser lines or LED peaks):
python
config_list = config.copy()
config_list["source"] = {
"wavelength": {
"mode": "list",
"values": [0.405, 0.450, 0.525, 0.590, 0.625, 0.680, 0.780],
},
"polarization": "unpolarized",
}
result_list = SingleRunner.run(config_list)Comparing different silicon thicknesses
Run sweeps for different silicon thicknesses and overlay:
python
import copy
thicknesses = [2.0, 3.0, 4.0]
results_by_thickness = []
for t in thicknesses:
cfg = copy.deepcopy(config)
cfg["pixel"]["layers"]["silicon"]["thickness"] = t
cfg["pixel"]["layers"]["silicon"]["dti"]["depth"] = t
r = SingleRunner.run(cfg)
results_by_thickness.append(r)
from compass.visualization.qe_plot import plot_qe_comparison
fig, ax = plt.subplots(figsize=(10, 6))
plot_qe_comparison(
results=results_by_thickness,
labels=[f"Si {t}um" for t in thicknesses],
ax=ax,
)
ax.set_title("QE vs Silicon Thickness")
plt.tight_layout()
plt.savefig("si_thickness_comparison.png", dpi=150)Key observations
| Wavelength range | Observation |
|---|---|
| 380-420 nm | Low QE due to surface recombination and shallow absorption |
| 420-500 nm | Blue channel peak. Short absorption depth, sensitive to BARL design |
| 500-580 nm | Green channel peak. Moderate absorption depth |
| 580-650 nm | Red channel peak. Longer absorption depth, benefits from thicker Si |
| 650-780 nm | QE drops as absorption depth exceeds silicon thickness |
TIP
For NIR (near-infrared) applications, increase silicon thickness to 5-6 um and extend the sweep to 1000 nm. You may need to add silicon material data beyond 780 nm.
Interactive QE Spectrum Chart
Explore how silicon thickness, BARL quality, and metal grid width affect the quantum efficiency spectrum of Red, Green, and Blue channels.
Blue peak QE:46.7%
Green peak QE:54.5%
Red peak QE:42.8%
Average QE:48.0%