# -*- coding: utf-8 -*-
################################################################
# The contents of this file are subject to the BSD 3Clause (New) License
# you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://directory.fsf.org/wiki/License:BSD_3Clause
# Software distributed under the License is distributed on an "AS IS"
# basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. See the
# License for the specific language governing rights and limitations
# under the License.
# The Original Code is part of the PyRadi toolkit.
# Initial Dev of the Original Code is Ricardo Augusto Tavares Santos, D.Sc.
# Instituto Tecnologico de Aeronautica Laboratorio de Guerra Eletronica - Brazil
# Portions created by Ricardo Augusto Tavares Santos are Copyright (C) 2012
# All Rights Reserved.
# Contributor(s): CJ Willers (refactored and updated original code).
###############################################################
"""
This model was built to give the user a simple but reliable tool to simulate or
to understand main parameters used to design a photovoltaic (PV) infrared
photodetector. All the work done in this model was based in classical equations
found in the literature.
See the __main__ function for examples of use.
The example suggested here uses InSb parameters found in the literature. For
every compound or material, all the parameters, as well as the bandgap equation
must be changed.
This code uses the scipy.constants physical constants. For more details see
https://docs.scipy.org/doc/scipy/reference/constants.html
This code does not yet fully comply with the coding standards
References:
[1] Infrared Detectors and Systems, EL Dereniak & GD Boreman, Wiley
[2] Infrared Detectors, A Rogalski (1st or 2nd Edition), CRC Press
[3] Band Parameters for III-V Compound Semiconductors and their Alloys,
I. Vurgaftmann, J. R. Meyer, and L. R. Ram-Mohan,
Journal of Applied Physics 89 11, pp. 5815-5875, 2001.
This package was partly developed to provide additional material in support of students
and readers of the book Electro-Optical System Analysis and Design: A Radiometry
Perspective, Cornelius J. Willers, ISBN 9780819495693, SPIE Monograph Volume
PM236, SPIE Press, 2013. http://spie.org/x648.html?product_id=2021423&origin_id=x646
"""
__version__ = '0.1.0'
__author__= 'pyradi team'
__all__= ['FermiDirac', 'JouleTeEv', 'eVtoJoule',
'Absorption', 'AbsorptionFile', 'QuantumEfficiency',
'Responsivity', 'DStar', 'NEP','Isaturation',
'EgVarshni', 'IXV', 'Noise','DstarSpectralFlatPhotonLim']
import sys
import scipy.constants as const
import numpy as np
################################################################################
#
[docs]
def eVtoJoule(EeV):
"""
Convert energy in eV to Joule.
Args:
| EeV: Energy in eV
Returns:
| EJ: Energy in J
"""
return EeV * const.e
################################################################################
#
[docs]
def JouleTeEv(EJ):
"""
Convert energy in Joule to eV.
Args:
| EJ: Energy in J
Returns:
| EeV: Energy in eV
"""
return EJ / const.e
################################################################################
#
[docs]
def FermiDirac(Ef, EJ, T):
"""
Returns the Fermi-Dirac probability distribution, given the crystal's
Fermi energy, the temperature and the energy where the distribution values
is required.
Args:
| Ef: Fermi energy in J
| EJ: Energy in J
| T : Temperature in K
Returns:
| fermiD : the Fermi-Dirac distribution
"""
#prevent divide by zero
den = (1 + np.exp( ( EJ - Ef ) / ( T * const.k) ) )
return 1 / den
################################################################################
#
[docs]
def Absorption(wavelength, Eg, tempDet, a0, a0p):
"""
Calculate the spectral absorption coefficient
for a semiconductor material with given material values.
The model used here is based on Equations 3.5, 3.6 in Dereniaks book.
Args:
| wavelength: spectral variable [m]
| Eg: bandgap energy [Ev]
| tempDet: detector's temperature in [K]
| a0: absorption coefficient [m-1] (Dereniak Eq 3.5 & 3.6)
| a0p: absorption coefficient in [m-1] (Dereniak Eq 3.5 & 3.6)
Returns:
| absorption: spectral absorption coefficient in [m-1]
"""
#frequency/wavelength expressed as energy in Ev
E = const.h * const.c / (wavelength * const.e )
# the np.abs() in the following code is to prevent nan and inf values
# the effect of the abs() is corrected further down when we select
# only the appropriate values based on E >= Eg and E < Eg
# Absorption coef - eq. 3.5- Dereniak
a35 = (a0 * np.sqrt(np.abs(E - Eg))) + a0p
# Absorption coef - eq. 3.6- Dereniak
a36 = a0p * np.exp((- np.abs(E - Eg)) / (const.k * tempDet))
absorption = a35 * (E >= Eg) + a36 * (E < Eg)
return absorption
################################################################################
#
[docs]
def AbsorptionFile(wavelength, filename):
"""
Read the absorption coefficient from a data file and interpolate on the
input spectral range.
The data file must have the wavelength in the first column and absorption
coefficient in [m-1] in the second column.
Args:
| wavelength: spectral variable [m]
| filename: file containing the data
Returns:
| wavelength: values where absorption is defined
| absorption: spectral absorption coefficient in [m-1]
"""
#load data and build interpolation table
from scipy.interpolate import interp1d
filedata = np.loadtxt(filename)
Table = interp1d(filedata[:,0], filedata[:,1], kind = 'linear')
#check for valid wavelengths values wrt the input file
mask = np.all([ wavelength >= np.min(filedata[:,0]) ,
wavelength <= np.max(filedata[:,0])], axis=0)
#select valid wavelengths
wavelenOut = wavelength[mask]
return wavelenOut, Table(wavelenOut)
################################################################################
#
[docs]
def QuantumEfficiency(absorption, d1, d2, theta1, nFront, nMaterial):
"""
Calculate the spectral quantum efficiency (QE) for a semiconductor material
with given absorption and material values.
Args:
| absorption: spectral absorption coefficient in [m-1]
| d1: depth where the detector depletion layer starts [m]
| d2: depth where the detector depletion layer ends [m]
| theta1: angle between the surface's normal and the radiation in radians
| nFront: index of refraction of the material in front of detector
| nMaterial: index of refraction of the detector material
Returns:
| quantumEffic: spectral quantum efficiency
"""
# calculate the semiconductor's optical reflectance
theta2 = np.arcsin(np.sin(theta1) * nFront / nMaterial) # Snell's equation
# Reflectance for perpendicular polarization
RS = np.abs((nFront * np.cos(theta1) - nMaterial * np.cos(theta2)) / \
(nFront * np.cos(theta1) + nMaterial * np.cos(theta2))) **2
# Reflectance for parallel polarization
RP = np.abs((nFront * np.cos(theta2) - nMaterial * np.cos(theta1)) / \
(nFront * np.cos(theta1) + nMaterial * np.cos(theta2))) ** 2
R = (RS + RP) / 2
# QE - eq. 3.4 - [1]
quantumEffic = (1 - R) * \
(np.exp( - absorption * d1) - np.exp( - absorption * d2))
return quantumEffic
################################################################################
#
[docs]
def Responsivity(wavelength, quantumEffic):
"""
Responsivity quantifies the amount of output seen per watt of radiant
optical power input [1]. But, for this application it is interesting to
define spectral responsivity that is the output per watt of monochromatic
radiation.
The model used here is based on Equations 7.114 in Dereniak's book.
Args:
| wavelength: spectral variable [m]
| quantumEffic: spectral quantum efficiency
Returns:
| responsivity in [A/W]
"""
return (const.e * wavelength * quantumEffic) / (const.h * const.c)
################################################################################
#
[docs]
def DStar(areaDet, deltaFreq, iNoise, responsivity):
r"""
The spectral D* is the signal-to-noise output when 1 W of monochromatic
radiant flux is incident on 1 cm2 detector area, within a
noise-equivalent bandwidth of 1 Hz.
Args:
| areaDet: detector's area in [m2]
| deltaFreq: measurement or desirable bandwidth - [Hz]
| iNoise: noise current [A]
| responsivity: spectral responsivity in [A/W]
Returns
| detectivity [cm \sqrt[Hz] / W] (note units)
"""
return (1e2 * responsivity * np.sqrt(areaDet * deltaFreq)) / (iNoise)
################################################################################
#
[docs]
def NEP(iNoise, responsivity):
"""
NEP is the radiant power incident on detector that yields SNR=1 [1].
Args:
| iNoise: noise current [A]
| responsivity: spectral responsivity in [A/W]
Returns
| spectral noise equivalent power [W]
"""
detectivity = responsivity / iNoise
#the strange '+ (detectivity==0)' code below is to prevent divide by zero
nep = ((1 / (detectivity + (detectivity == 0))) * (detectivity != 0)) + \
sys.float_info.max/10 * (detectivity == 0)
return nep, detectivity
################################################################################
#
[docs]
def Isaturation(mobE, tauE, mobH, tauH, me, mh, na, nd, Eg, tDetec, areaDet):
"""
This function calculates the reverse saturation current, by
Equation 7.22 in Dereniak's book
Args:
| mobE: electron mobility [m2/V.s]
| tauE: electron lifetime [s]
| mobH: hole mobility [m2/V.s]
| tauH: hole lifetime [s]
| me: electron effective mass [kg]
| mh: hole effective mass [kg]
| na: acceptor concentration [m-3]
| nd: donor concentration [m-3]
| Eg: energy bandgap in [Ev]
| tDetec: detector's temperature in [K]
| areaDet: detector's area [m2]
Returns:
| I0: reverse sat current [A]
"""
# diffusion length [m] Dereniak Eq7.19
Le=np.sqrt(const.k * tDetec * mobE * tauE / const.e)
Lh=np.sqrt(const.k * tDetec * mobH * tauH / const.e)
# intrinsic carrier concentration - Dereniak`s book eq. 7.1 - m-3
# Eg here in eV units, multiply with q
ni = (np.sqrt(4 * (2 * np.pi * const.k * tDetec / const.h ** 2) ** 3 *\
np.exp( - (Eg * const.e) / (const.k * tDetec)) * (me * mh) ** 1.5))
# reverse saturation current - Dereniak eq. 7.22 - Ampère
if na == 0 or tauE == 0:
elec = 0
else:
elec = Le / (na * tauE)
if nd == 0 or tauH == 0:
hole = 0
else:
hole = Lh / ( nd * tauH )
I0 = areaDet * const.e * (ni ** 2) *( elec + hole )
return (I0,ni)
################################################################################
#
[docs]
def EgVarshni(E0, VarshniA, VarshniB, tempDet):
"""
This function calculates the bandgap at detector temperature, using the
Varshni equation
Args:
| E0: band gap at room temperature [eV]
| VarshniA: Varshni parameter
| VarshniB: Varshni parameter
| tempDet: detector operating temperature [K]
Returns:
| Eg: bandgap at stated temperature [eV]
"""
return (E0 - (VarshniA * (tempDet ** 2 / (tempDet + VarshniB))))
################################################################################
#
[docs]
def IXV(V, IVbeta, tDetec, iPhoto,I0):
"""
This function provides the diode curve for a given photocurrent.
The same function is also used to calculate the dark current, using
IVbeta=1 and iPhoto=0
Args:
| V: bias [V]
| IVbeta: diode equation non linearity factor;
| tDetec: detector's temperature [K]
| iPhoto: photo-induced current, added to diode curve [A]
| I0: reverse sat current [A]
Returns:
| current from detector [A]
"""
# diode equation from Dereniak's book eq. 7.23
return I0 * (np.exp(const.e * V / (IVbeta * const.k * tDetec)) - 1) - iPhoto
################################################################################
#
[docs]
def Noise(tempDet, IVbeta, Isat, iPhoto, vBias=0):
"""
This function calculates the noise power spectral density produced in the
diode: shot noise and thermal noise. The assumption is that all noise
sources are white noise PSD.
Eq 5.143 plus thermal noise, see Eq 5.148
Args:
| tempDet: detector's temperature [K]
| IVbeta: detector nonideal factor [-]
| Isat: reverse saturation current [A]
| iPhoto: photo current [A]
| vBias: bias voltage on the detector [V]
Returns:
| detector noise power spectral density [A/Hz1/2]
| R0: dynamic resistance at zero bias.
| Johnson noise only noise power spectral density [A/Hz1/2]
| Shot noise only noise power spectral density [A/Hz1/2]
"""
R0 = IVbeta * const.k * tempDet / (Isat * const.e)
# johnson noise
iJohnson = 4 * const.k * tempDet / R0
# shot noise for thermal component Isat
iShot1 = 2 * const.e * Isat
# shot noise for thermal component Isat
iShot2 = 2 * const.e * Isat *np.exp(const.e * vBias /(const.k * tempDet * IVbeta))
# shot noise for photocurrent
iShot3 = 2 * const.e * iPhoto
# total noise
noise = np.sqrt(iJohnson + iShot1 + iShot2 + iShot3 )
return noise, R0, np.sqrt(iJohnson), np.sqrt(iShot1 + iShot2 + iShot3 )
################################################################################
#
[docs]
def DstarSpectralFlatPhotonLim(Tdetec, Tenvironment,epsilon):
r"""
This function calculates the photon noise limited D* of a detector with
unlimited spectral response. This case does not apply to photon detectors.
The absorption is assumed spectrally flat.
Args:
| Tdetec: detector temperature [K]
| Tenvironment: environment temperature [K]
| epsilon: emissivity/absorption
Returns:
| D* [cm \sqrt[Hz] / W] (note units)
"""
# Kruse, P. W., Uncooled Thermal Imaging Arrays, Systems, and Applications ,
#no. TT51, SPIE Press (2001).
return 100 * np.sqrt(epsilon/(8 * const.k * const.sigma * \
(Tdetec ** 5 + Tenvironment ** 5)))
################################################################################
if __name__ == '__main__':
import os as _os
_clutter = _os.path.join(_os.path.dirname(_os.path.abspath(__file__)), 'clutter')
_os.makedirs(_clutter, exist_ok=True)
def _c(fname):
"""Redirect an output filename into the clutter folder."""
return _os.path.join(_clutter, _os.path.basename(str(fname)))
"""
In the model application, the user must define all the detector and
semiconductor parameters. Each material type has its own paramenters,
"""
import pyradi.ryplanck as ryplanck
import pyradi.ryplot as ryplot
#calculate the theoretical D* for a spectrally flat detector
Tenvironment = np.linspace(1,600,100)
Tdetector = [0, 77, 195, 290]
dstarP = ryplot.Plotter(1,1,1,figsize=(5,2))
for Tdetec in Tdetector:
dStar = DstarSpectralFlatPhotonLim(Tdetec,Tenvironment,1)
dstarP.semilogY(1,Tenvironment,dStar,'',\
r'Environmental temperature [K]',r'D* [cm.\sqrt{Hz}/W]',
pltaxis=[0, 600, 1e9, 1e13])
currentP = dstarP.getSubPlot(1)
for xmaj in currentP.xaxis.get_majorticklocs():
currentP.axvline(x=xmaj,ls='-')
for ymaj in currentP.yaxis.get_majorticklocs():
currentP.axhline(y=ymaj,ls='-')
dstarP.saveFig(_c('dstarFlat.eps'))
######################################################################
# tempBack = np.asarray([1])
tempBack = np.asarray([1, 2, 4, 10, 25, 77, 195, 300, 500])
eta = 1
wavelength = np.logspace(0, 3, 100, True, 10)
dstarwlc = np.zeros(wavelength.shape)
def deeStarPeak(wavelength,temperature,eta,halfApexAngle):
i = 0
for wlc in wavelength:
wl = np.linspace(wlc/100, wlc, 1000).reshape(-1, 1)
LbackLambda = ryplanck.planck(wl,temperature, type='ql')/np.pi
Lback = np.trapezoid(LbackLambda.reshape(-1, 1),wl, axis=0)[0]
Eback = Lback * np.pi * (np.sin(halfApexAngle)) ** 2
# funny construct is to prevent divide by zero
tempvar = np.sqrt(eta/(Eback+(Eback==0))) * (Eback!=0) + 0 * (Eback==0)
dstarwlc[i] = 1e-6 * wlc * tempvar/(const.h * const.c * np.sqrt(2))
#print(Eback)
i = i + 1
return dstarwlc * 100. # to get cm units
dstarP = ryplot.Plotter(1,1,1)
for temperature in tempBack:
#halfApexAngle = 90 deg is for hemisphere
halfApexAngle = (90 / 180.) * np.pi
dstar = deeStarPeak(wavelength,temperature,eta,halfApexAngle)
dstarP.logLog(1,wavelength,dstar,'',\
r'Peak wavelength [$\mu$m]',r'D* [cm.$\sqrt{\rm Hz}$/W]',pltaxis=[1, 1e3, 1e10, 1e20])
currentP = dstarP.getSubPlot(1)
for xmaj in currentP.xaxis.get_majorticklocs():
currentP.axvline(x=xmaj,ls='-')
for ymaj in currentP.yaxis.get_majorticklocs():
currentP.axhline(y=ymaj,ls='-')
dstarP.saveFig(_c('dstarPeak.eps'))
#####################################################################
#demonstrate and test absorption data read from file
absFile = ryplot.Plotter(1,1,1)
wavelength = np.linspace(0.2, 2, 600)
filenames = [
'data/absorptioncoeff/Si.txt',
'data/absorptioncoeff/GaAs.txt',
'data/absorptioncoeff/Ge.txt',
'data/absorptioncoeff/In07Ga03As64P36.txt',
'data/absorptioncoeff/InP.txt',
'data/absorptioncoeff/In53Ga47As.txt',
'data/absorptioncoeff/piprekGe.txt',
'data/absorptioncoeff/piprekGaAs.txt',
'data/absorptioncoeff/piprekSi.txt',
'data/absorptioncoeff/piprekInP.txt'
]
for filename in filenames:
wl, absorb = AbsorptionFile(wavelength, filename)
absFile.semilogY(1,wl,absorb,'Absorption coefficient',\
r'Wavelength [$\mu$m]','Absorptance [m-1]')
currentP = absFile.getSubPlot(1)
for xmaj in currentP.xaxis.get_majorticklocs():
currentP.axvline(x=xmaj,ls='-')
for ymaj in currentP.yaxis.get_majorticklocs():
currentP.axhline(y=ymaj,ls='-')
absFile.saveFig(_c('absorption.eps'))
#######################################################################
#plot the Fermi-Dirac distribution
temperature = [0.001, 77, 300]
EevR = np.linspace(-0.2, 0.2, 500)
fDirac = FermiDirac(0, eVtoJoule(EevR), temperature[0]).reshape(-1, 1)
legend = [f"{temperature[0]:.0f} K"]
for temp in temperature[1:] :
fDirac = np.hstack((fDirac, FermiDirac(0, eVtoJoule(EevR), temp).reshape(-1, 1)))
legend.append(f"{temp:.0f} K")
# Mel = planck(wl, temperature[0], type='el').reshape(-1, 1) # [W/(m$^2$.$\mu$m)]
fDfig = ryplot.Plotter(1,1,1)
fDfig.plot(1,EevR,fDirac,'Fermi-Dirac distribution function',\
r'Energy [eV]','Occupancy probability', label=legend)
fDfig.saveFig(_c('FermiDirac.eps'))
######################################################################
######################################################################
######################################################################
######################################################################
######################################################################
#now calculate the characteristics of an InSb detector
#wavelength in micrometers, remember to scale down in functions.
wavelenInit = 1 # wavelength in um
wavelenFinal = 5.5 # wavelength in um
wavelength = np.linspace(wavelenInit, wavelenFinal, 200)
Vbias = np.linspace(-200e-3, 100e-3, 200) # bias voltage range
#source properties
tempSource = 2000 # source temperature in K
emisSource = 1.0 # source emissivity
distance = 0.1 # distance between source and detector
areaSource = 0.000033 # source area in m2
solidAngSource = areaSource / (distance ** 2)
#background properties
tempBkg = 280.0 # background temperature in K
emisBkg = 1.0 # background emissivity
solidAngBkg = np.pi # background is hemisphere
#test setup parameters
deltaFreq = 100.0 # measurement or desirable bandwidth - Hertz
theta1 = 0.0 # radiation incident angle in radians
# detector device parameters
tempDet = 80.0 # detector temperature in K
areaDet = (200e-6) ** 2 # detector area in m2
d1 = 0 # start of the detector depletion layer in meter
d2 = 5e-6 # end of the detector depletion layer in meter
n1 = 1.0 # refraction index of the air
# detector material properties for InSb
E0 = 0.24 # semiconductor bandgap at room temp in Ev
VarshniA = 6e-4 # first fitting parameter for the Varshni's Equation
VarshniB = 500.0 # second fitting parameter for the Varshni's Equation
#Varshni parameters taken from:
#Group IV Elements, IV--IV and III--V Compounds. Part b --- Electronic,
#Transport, Optical and Other Properties, O. Madelung and U. R\"{o}ssler and M. Schulz,
#Springer-Verlag, 2002.
n2 = 3.42 # refraction index of the semiconductor being analyzed
a0 = 1.9e4 * 100 # absorption coefficient [m-1], Eq3.5 & 3.6 Dereniak
a0p = 800 * 100 # absorption coefficient [m-1] Eq3.5 & 3.6 Dereniak
eMob = 100.0 # electron mobility - m2/V.s
hMob = 1.0 # hole mobility - m2/V.s
tauE = 1e-8 # electron lifetime - s
tauH = 1e-8 # electron lifetime - s
me = 0.014 * const.m_e # electron effective mass
mh = 0.43 * const.m_e # hole effective mass
na = 1e16 # doping = acceptor concentration - m-3
nd = 1e16 # doping = donor concentration - m-3
IVbeta = 1.7 # 1 = dominanr diffusion current, 2 = g-r dominant
#######################################################################
# bandgap at operating termperature
Eg = EgVarshni(E0, VarshniA, VarshniB, tempDet)
print(f'Bandgap = {Eg:.6f}')
print(f'Lambda_c= {1.24 / Eg:.6f}')
#calculate the spectral absorption, quantum efficiency and responsivity
absorption = Absorption(wavelength / 1e6, Eg, tempDet, a0, a0p)
quantumEffic = QuantumEfficiency(absorption, d1, d2, theta1, n1, n2)
responsivity = Responsivity(wavelength / 1e6,quantumEffic)
print (f"\nSource temperature = {tempSource} K")
print (f"Source emissivity = {emisSource} ")
print (f"Source distance = {distance} m")
print (f"Source area = {areaSource} m2")
print (f"Source solid angle = {solidAngSource:.6f} sr")
print (f"\nBackground temperature = {tempBkg} K")
print (f"Background emissivity = {emisBkg} ")
print (f"Background solid angle = {solidAngBkg:.6f} sr")
#spectral irradiance for test setup, for both source and background
# in units of photon rate q/(s.m2)
EsourceQL =(emisSource * ryplanck.planck(wavelength,tempSource,'ql') \
* solidAngSource) / (np.pi )
EbkgQL = (emisBkg * ryplanck.planck(wavelength, tempBkg, 'ql') * \
solidAngBkg ) / (np.pi )
#in radiant units W/m2
EsourceEL = (emisSource * ryplanck.planck(wavelength, tempSource,'el')*\
solidAngSource) / (np.pi )
EbkgEL = (emisBkg * ryplanck.planck(wavelength, tempBkg, 'el') * \
solidAngBkg) / (np.pi )
#photocurrent from both QE&QL and R&EL spectral data should have same values.
iSourceE = np.trapezoid(EsourceEL * areaDet * responsivity, wavelength)
iBkgE = np.trapezoid(EbkgEL * areaDet * responsivity, wavelength)
iSourceQ = np.trapezoid(EsourceQL * areaDet * quantumEffic * const.e,wavelength)
iBkgQ = np.trapezoid(EbkgQL * areaDet * quantumEffic * const.e, wavelength)
iTotalE = iSourceE + iBkgE
iTotalQ = iSourceQ + iBkgQ
print (f"\nDetector current source = {iSourceQ:.6f} A {iSourceE:.6f} A")
print (f"Detector current background = {iBkgQ:.6f} A {iBkgE:.6f} A")
print (f"Detector current total = {iTotalQ:.6f} A {iTotalE:.6f} A")
#saturation current from material and detector parameters
Isat,ni = Isaturation(eMob, tauE, hMob, tauH, me, mh, na, nd, Eg, tempDet, areaDet)
print (f"\nI0 = {Isat:.6f} [A] ")
print (f"ni = {ni:.6f} [m-3] ")
#calculate the current-voltage curve for dark, background only and total signal
ixvDark = IXV(Vbias, IVbeta, tempDet, 0, Isat)
ixvBackgnd = IXV(Vbias, IVbeta, tempDet, iBkgE, Isat)
ixvTotalE = IXV(Vbias, IVbeta, tempDet, iTotalE, Isat)
#now calculate the noise
iNoisePsd, R0, johnsonPsd, shotPsd = Noise(tempDet, IVbeta, Isat, iBkgE)
iNoise = iNoisePsd * np.sqrt(deltaFreq)
print(f'R0= = {R0:.6e} Ohm')
print(f'NBW= = {deltaFreq:.6e} Hz')
print(f"iNoiseTotal = {iNoise:.6e} A")
print(f"iNoise Johnson= = {johnsonPsd * np.sqrt(deltaFreq):.6e} A")
print(f"iNoise shot = {shotPsd * np.sqrt(deltaFreq):.6e} A")
#calculate the spectral noise equivalent power
NEPower, detectivity = NEP(iNoise, responsivity)
#use either of the following two forms:
#dStar = DStar(areaDet, deltaFreq, iNoise, responsivity) # units in cm
dStar = DStar(areaDet, 1, iNoisePsd, responsivity) # units in cm
absFig = ryplot.Plotter(1,1,1)
absFig.semilogY(1,wavelength,absorption,'Absorption Coefficient',\
r'Wavelength [$\mu$m]',r'Absorption Coefficient [m$^{-1}$]')
absFig.saveFig(_c('spectralabsorption.eps'))
QE = ryplot.Plotter(1,1,1)
QE.plot(1,wavelength,quantumEffic,'Spectral Quantum Efficiency',\
r'Wavelength [$\mu$m]','Quantum Efficiency')
QE.saveFig(_c('QE.eps'))
IXVplot = ryplot.Plotter(1,1,1)
IXVplot.semilogY(1,Vbias,np.abs(ixvDark) * 1e6, plotCol=['g'],label=['Dark'])
IXVplot.semilogY(1,Vbias,np.abs(ixvBackgnd) * 1e6, plotCol=['r'],label=['Background'])
IXVplot.semilogY(1,Vbias,np.abs(ixvTotalE) * 1e6,'IxV Curve',\
'Voltage [V]','|Current| [uA]',plotCol=['b'],label=['Target'])
IXVplot.saveFig(_c('IXVplot.eps'))
Respons = ryplot.Plotter(1,1,1)
Respons.plot(1,wavelength,responsivity,'Spectral Responsivity',\
r'Wavelength [$\mu$m]','Responsivity [A/W]')
Respons.saveFig(_c('Responsivity.eps'))
DStarfig = ryplot.Plotter(1,1,1)
DStarfig.plot(1,wavelength,dStar,'Spectral normalized detectivity',\
r'Wavelength [$\mu$m]',r'D* [cm$\sqrt{{\rm Hz}}$/W]')
DStarfig.saveFig(_c('dStar.eps'))
NEPfig = ryplot.Plotter(1,1,1)
NEPfig.plot(1,wavelength,NEPower,'Spectral Noise Equivalent Power',\
r'Wavelength [$\mu$m]','NEP [W]',\
pltaxis=[wavelenInit, wavelenFinal, 0,NEPower[0]])
NEPfig.saveFig(_c('NEP.eps'))
print('\nDone!')
