e2v_ccd203

optika.sensors.materials.e2v_ccd203(temperature=<Quantity 300. K>)

A model of the light-sensitive material of the custom e2v CCD sensors on board the Atmospheric Imaging Assembly [Lemen et al., 2012] from Boerner et al. [2012]

This model uses e2v_ccd64_thick() to represent the depletion region.

Examples

Plot the measured AIA CCD quantum efficiency vs the fitted quantum efficiency calculated using the method of Stern et al. [1994].

import matplotlib.pyplot as plt
import astropy.units as u
import astropy.visualization
import named_arrays as na
import optika

# Create a new instance of the e2v CCD97 light-sensitive material
material = optika.sensors.materials.e2v_ccd203()

# Store the wavelengths at which the QE was measured
wavelength_measured = material.eqe_measured.inputs

# Store the QE measurements
eqe_measured = material.eqe_measured.outputs

# Define a grid of wavelengths with which to evaluate the fitted QE
wavelength_fit = na.geomspace(10, 10000, axis="wavelength", num=1001) * u.AA

# Evaluate the fitted QE using the given wavelengths
eqe_fit = material.quantum_efficiency_effective(
    rays=optika.rays.RayVectorArray(
        wavelength=wavelength_fit,
        direction=na.Cartesian3dVectorArray(0, 0, 1),
    ),
    normal=na.Cartesian3dVectorArray(0, 0, -1),
)

# Plot the measured QE vs the fitted QE
with astropy.visualization.quantity_support():
    fig, ax = plt.subplots(constrained_layout=True)
    na.plt.scatter(
        wavelength_measured,
        eqe_measured,
        label="measured",
    )
    na.plt.plot(
        wavelength_fit,
        eqe_fit,
        label="fit",
    )
    ax.set_xscale("log")
    ax.set_xlabel(f"wavelength ({wavelength_fit.unit:latex_inline})")
    ax.set_ylabel("quantum efficiency")
    ax.legend()

The thickness of the oxide layer found by the fit is

material.thickness_oxide

\[14.157596 \; \mathrm{\mathring{A}}\]

The thickness of the implant layer found by the fit is

material.thickness_implant

\[3346.4431 \; \mathrm{\mathring{A}}\]

The thickness of the substrate is

material.thickness_substrate

\[16 \; \mathrm{\mu m}\]

The differential charge collection efficiency at the backsurface found by the fit is

material.cce_backsurface

\[0.45244594 \; \mathrm{}\]

And the roughness of the substrate found by the fit is

material.roughness_substrate

\[1.0885736 \; \mathrm{nm}\]

Plot the width of the charge diffusion kernel for this sensor as a function of wavelength.

# Compute the width of the charge diffusion kernel
# for each wavelength.
width = material.width_charge_diffusion(
    rays=optika.rays.RayVectorArray(
        wavelength=wavelength_fit,
        direction=na.Cartesian3dVectorArray(0, 0, 1),
    ),
    normal=na.Cartesian3dVectorArray(0, 0, -1),
)

# Plot the results
with astropy.visualization.quantity_support():
    fig, ax = plt.subplots()
    na.plt.plot(
        wavelength_fit,
        width,
        ax=ax,
    )
    ax.set_xscale("log")
    ax.set_xlabel(f"wavelength ({ax.get_xlabel()})")
    ax.set_ylabel(f"width ({ax.get_ylabel()})")

References to optika.sensors.materials.e2v_ccd203
e2v_ccd203

Parameters:

temperature (Quantity | AbstractScalar)

Return type:

BackIlluminatedSiliconSensorMaterial