Added optika.sensors.E2VCCD97Material class. (#6)

Added `optika.sensors.E2VCCD97Material` class.

docs/refs.bib

@@ -90,3 +90,11 @@ @article{Stern1994
     doi = {10.1364/AO.33.002521},
     abstract = {We report quantum efficiency measurements of backilluminated, ion-implanted, laser-annealed CCD's in the wavelength range 13--10,000 {\AA}. The equivalent quantum efficiency (the equivalent photons detected per incident photon) ranges from a minimum of 5\% at 1216 {\AA} to a maximum of 87\% at 135 {\AA}. Using a simple relationship for the charge-collection efficiency of the CCD pixels as a function of depth, we present a semiempirical model with few parameters that reproduces our measurements with a fair degree of accuracy. The advantage of this model is that it can be used to predict CCD quantum efficiency performance for shallow backside implanted devices without a detailed solution of a system of differential equations, as in conventional approaches, and it yields a simple analytic form for the charge-collection efficiency that is adequate for detector calibration purposes. Making detailed assumptions about the dopant profile, we also solve the current density and continuity equations in order to relate our semiempirical model parameters to surface and bulk device properties. The latter procedure helps to better establish device processing parameters for a given level of CCD quantum efficiency performance.},
 }
+@techreport{Moody2017,
+    author = {Ian Moody and Marc Watkins and Ray Bell and Matthew Soman and Jonathan Keelan and Andrew Holland},
+    title = {CCD QE in the Soft X-ray Range},
+    institution = {e2v},
+    month={March},
+    year={2017},
+    url={https://oro.open.ac.uk/49003/1/Moody%202017%20-%20SXRQE_WhitePaper.pdf},
+}

optika/sensors/_materials/__init__.py

@@ -1 +1,2 @@
 from ._materials import *
+from ._e2v_ccd97 import *

optika/sensors/_materials/_e2v_ccd97/__init__.py

@@ -0,0 +1 @@
+from ._e2v_ccd97 import *

optika/sensors/_materials/_e2v_ccd97/_e2v_ccd97.py

@@ -0,0 +1,184 @@
+import functools
+import pathlib
+import numpy as np
+import scipy.optimize
+import astropy.units as u
+import named_arrays as na
+from .._materials import quantum_efficiency_effective
+from .._materials import AbstractBackilluminatedCCDMaterial
+
+__all__ = [
+    "E2VCCD97Material",
+]
+
+
+class E2VCCD97Material(
+    AbstractBackilluminatedCCDMaterial,
+):
+    """
+    A model of the light-sensitive material of an e2v CCD90 sensor based on
+    measurements by :cite:t:`Moody2017`.
+
+    Examples
+    --------
+
+    Plot the measured E2VCCD97 quantum efficiency vs the fitted
+    quantum efficiency calculated using the method of :cite:t:`Stern1994`.
+
+    .. jupyter-execute::
+
+        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_ccd97 = optika.sensors.E2VCCD97Material()
+
+        # Store the wavelengths at which the QE was measured
+        wavelength_measured = material_ccd97.quantum_efficiency_measured.inputs
+
+        # Store the QE measurements
+        qe_measured = material_ccd97.quantum_efficiency_measured.outputs
+
+        # Define a grid of wavelengths with which to evaluate the fitted QE
+        wavelength_fit = na.geomspace(5, 10000, axis="wavelength", num=1001) * u.AA
+
+        # Evaluate the fitted QE using the given wavelengths
+        qe_fit = material_ccd97.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,
+                qe_measured,
+                label="measured",
+            )
+            na.plt.plot(
+                wavelength_fit,
+                qe_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
+
+    .. jupyter-execute::
+
+        material_ccd97.thickness_oxide
+
+    The thickness of the implant layer found by the fit is
+
+    .. jupyter-execute::
+
+        material_ccd97.thickness_implant
+
+    The thickness of the substrate found by the fit is
+
+    .. jupyter-execute::
+
+        material_ccd97.thickness_substrate
+
+    And the differential charge collection efficiency at the backsurface
+    found by the fit is
+
+    .. jupyter-execute::
+
+        material_ccd97.cce_backsurface
+    """
+
+    @property
+    def quantum_efficiency_measured(self) -> na.FunctionArray:
+        directory = pathlib.Path(__file__).parent
+        energy, qe = np.genfromtxt(
+            fname=directory / "e2v_ccd97_qe_moody2017.csv",
+            delimiter=", ",
+            unpack=True,
+        )
+        energy = energy << u.eV
+        wavelength = energy.to(u.AA, equivalencies=u.spectral())
+        return na.FunctionArray(
+            inputs=na.ScalarArray(wavelength, axes="wavelength"),
+            outputs=na.ScalarArray(qe, axes="wavelength"),
+        )
+
+    @functools.cached_property
+    def _quantum_efficiency_fit(self) -> dict[str, float | u.Quantity]:
+        qe_measured = self.quantum_efficiency_measured
+
+        unit_thickness_oxide = u.AA
+        unit_thickness_implant = u.AA
+        unit_thickness_substrate = u.um
+
+        def eqe_rms_difference(x: tuple[float, float, float, float]):
+            (
+                thickness_oxide,
+                thickness_implant,
+                thickness_substrate,
+                cce_backsurface,
+            ) = x
+            qe_fit = quantum_efficiency_effective(
+                wavelength=qe_measured.inputs,
+                direction=na.Cartesian3dVectorArray(0, 0, 1),
+                thickness_oxide=thickness_oxide << unit_thickness_oxide,
+                thickness_implant=thickness_implant << unit_thickness_implant,
+                thickness_substrate=thickness_substrate << unit_thickness_substrate,
+                cce_backsurface=cce_backsurface,
+            )
+
+            return np.sqrt(np.mean(np.square(qe_measured.outputs - qe_fit))).ndarray
+
+        thickness_oxide_guess = 50 * u.AA
+        thickness_implant_guess = 2317 * u.AA
+        thickness_substrate_guess = 7 * u.um
+        cce_backsurface_guess = 0.21
+
+        fit = scipy.optimize.minimize(
+            fun=eqe_rms_difference,
+            x0=[
+                thickness_oxide_guess.to_value(unit_thickness_oxide),
+                thickness_implant_guess.to_value(unit_thickness_implant),
+                thickness_substrate_guess.to_value(unit_thickness_substrate),
+                cce_backsurface_guess,
+            ],
+            method="nelder-mead",
+        )
+
+        thickness_oxide, thickness_implant, thickness_substrate, cce_backsurface = fit.x
+        thickness_oxide = thickness_oxide << unit_thickness_oxide
+        thickness_implant = thickness_implant << unit_thickness_implant
+        thickness_substrate = thickness_substrate << unit_thickness_substrate
+
+        return dict(
+            thickness_oxide=thickness_oxide,
+            thickness_implant=thickness_implant,
+            thickness_substrate=thickness_substrate,
+            cce_backsurface=cce_backsurface,
+        )
+
+    @property
+    def thickness_oxide(self) -> u.Quantity:
+        return self._quantum_efficiency_fit["thickness_oxide"]
+
+    @property
+    def thickness_implant(self) -> u.Quantity:
+        return self._quantum_efficiency_fit["thickness_implant"]
+
+    @property
+    def thickness_substrate(self) -> u.Quantity:
+        return self._quantum_efficiency_fit["thickness_substrate"]
+
+    @property
+    def cce_backsurface(self) -> float:
+        return self._quantum_efficiency_fit["cce_backsurface"]

optika/sensors/_materials/_e2v_ccd97/_e2v_ccd97_test.py

@@ -0,0 +1,15 @@
+import pytest
+import optika
+from .._materials_test import AbstractTestAbstractBackilluminatedCCDMaterial
+
+
+@pytest.mark.parametrize(
+    argnames="a",
+    argvalues=[
+        optika.sensors.E2VCCD97Material(),
+    ],
+)
+class TestE2VCCD97Material(
+    AbstractTestAbstractBackilluminatedCCDMaterial,
+):
+    pass

optika/sensors/_materials/_e2v_ccd97/e2v_ccd97_qe_moody2017.csv

@@ -0,0 +1,33 @@
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+599.7856496789133, 0.9291390728476823
+647.7313122091703, 0.960927152317881
+748.6231645365851, 0.9807947019867551
+901.183765633706, 0.9986754966887419
+1198.354915639953, 0.9907284768211922
+1397.6006827213541, 0.8894039735099339
+1593.5201660325251, 0.7741721854304637
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+1800.5399029139885, 0.6569536423841061
+1825.142004033248, 0.7463576158940399
+1833.4171803334832, 0.8735099337748347
+1833.4171803334832, 0.9172185430463577
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