09a-DetectorModelling.ipynb

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    "\\WPproject{Computational Radiometry}           % project name\n",
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    "\\WPsubject{09a InSb Detector Modelling}           % main heading \n",
    "\\WPconclusions{} \n",
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    "\\WPdocauthor{CJ Willers}\n",
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    "\\bibliography{09a-DetectorModelling}\n",
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   "source": [
    "# 9 Detector Modelling"
   ]
  },
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   "source": [
    "# to prepare the python environment\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display\n",
    "from IPython.display import Image\n",
    "from IPython.display import HTML\n",
    "\n",
    "import pyradi.rydetector as rydetector\n",
    "import pyradi.ryplot as ryplot\n",
    "import pyradi.ryplanck as ryplanck\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook forms part of a series on computational optical radiometry\n",
    "These notebooks are constantly revised and updated, please revisit from time to time.  \n",
    "\n",
    "The source for this notebook is available at   \n",
    "https://github.com/NelisW/ComputationalRadiometry/blob/master/09a-DetectorModelling.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The date of this document and module versions used in this document are given at the end of the file.  \n",
    "Feedback is appreciated: neliswillers at gmail dot com."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pyradi rydetector  module models the primary functionality of optical detectors.  This notebook demonstrates the development of a simple single element InSb detector model suitable for high-level system modelling.  The performance of the detector is described in terms of its figures of merit. The theoretical background to this model is covered the text book.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concept"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detector is modelled as a bulk material device, where a fraction of the incident photons are absorbed in the material and then converted to an electrical signal.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detector material properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the bandgap $E_g$ of the InSb material at the detector operating temperature (80 K), using \n",
    "(Eq 5.3) imlemented in rydetector.EgVarshni\n",
    "$$\n",
    "    E_g = E_{g}(0) -\\left(\\frac{A T^2}{T + B}\\right).\n",
    "$$ \n",
    "\n",
    "The Varshni model (Eq 5.3) is an approximated empirical fit to predict a semiconductor bandgap at a given temperature. The approximation employs three parameters: the reference bandgap at a\n",
    "given temperature (usually 0 K), plus two parameters to predict the bandgap at other temperatures (relative to the reference bandgap). Several sets of different Varshni parameters were proposed (Table A.6) by Madelung, Piprek, and Dhanaraj, for the references see my book) using different reference bandgap values, and also based on different experimental data. The bandgap calculation considers three different sets of feasible Varshni coefficients.\n",
    "\n",
    "The detector cutoff wavelength is calculated from $\\lambda_c = 1.24/E_g$.\n",
    "\n",
    "The data storage in the following cell is done in a Pandas dataframe, simply because it is easier to manipulate and display.  The detector parameters are entered as entries into the dataframe and then the Varshni equation is used to calculate Eg in a new row. Next the Eg values are used to calculate the cutoff wavelength $\\lambda_c$.  All the data are neatly then displayed in the HTML table."
   ]
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     "text": [
      "Madelung's value for Eg 0.2333793103448276 is used in the rest of this analysis.\n",
      "lambda_c is 5.3132387706855795.\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Madelung</th>\n",
       "      <th>Piprek</th>\n",
       "      <th>Dhanaraj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Eg(0)</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eg</th>\n",
       "      <td>0.233379</td>\n",
       "      <td>0.226808</td>\n",
       "      <td>0.231271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\lambda_c$</th>\n",
       "      <td>5.313239</td>\n",
       "      <td>5.467179</td>\n",
       "      <td>5.361683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Madelung      Piprek    Dhanaraj\n",
       "Eg(0)          0.240000    0.235000    0.235000\n",
       "A              0.000600    0.000320    0.000310\n",
       "B            500.000000  170.000000  452.000000\n",
       "T             80.000000   80.000000   80.000000\n",
       "Eg             0.233379    0.226808    0.231271\n",
       "$\\lambda_c$    5.313239    5.467179    5.361683"
      ]
     },
     "execution_count": 61,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to calculate the semiconductor properties\n",
    "tempDet = 80  # detector temperature in K\n",
    "varshni = pd.DataFrame({ 'Madelung' : pd.Series([0.24, 6e-4, 500, tempDet], index=['Eg(0)','A','B','T']),\n",
    "           'Piprek' : pd.Series([0.235, 3.2e-4, 170, tempDet], index=['Eg(0)','A','B','T']),\n",
    "           'Dhanaraj' : pd.Series([0.235, 3.1e-4, 452, tempDet], index=['Eg(0)','A','B','T'])})\n",
    "eg = pd.DataFrame(varshni.apply(lambda x: rydetector.EgVarshni(x['Eg(0)'], x['A'], x['B'], x['T']), axis=0)).T\n",
    "lambdaC = 1.24 / eg\n",
    "varshni = pd.concat([varshni, eg], axis=0).rename({0:'Eg'})\n",
    "varshni = pd.concat([varshni, lambdaC], axis=0).rename({0:'$\\lambda_c$'})\n",
    "Eg = varshni['Madelung']['Eg']\n",
    "print(\"Madelung's value for Eg {} is used in the rest of this analysis.\".format(Eg))\n",
    "print(\"lambda_c is {}.\".format(1.24/Eg))\n",
    "varshni"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spectral absorption coefficient is calculated by using (Eq 5.78) and (Eq 5.79)\n",
    "\n",
    "$$\n",
    "\\alpha_{\\nu} \\propto \\sqrt{h\\nu-E_g} \\approx \\alpha_{\\lambda_c} +  \\alpha_0\\sqrt{h\\nu-E_g} \\;\\; {\\rm for} \\;\\; h\\nu>E_g.\n",
    "$$\n",
    "and \n",
    "$$\n",
    "\\alpha_{\\nu} = \\alpha_{\\lambda_c}\\exp\\left[\\frac{h\\nu-E_g}{kT}\\right] \\;\\; {\\rm for} \\;\\; h\\nu<E_g.\n",
    "$$\n",
    "the data in (Table A.6). \n",
    "\n",
    "The quantum efficiency is calculated by using (Eq 5.87 and 5.88)\n",
    "$$\n",
    "\\frac{J}{q E_q}=\\eta=(1-\\rho)(1-e^{- \\alpha d})\n",
    "$$\n",
    "and\n",
    "$$\n",
    "\\rho=\n",
    "\\left(\n",
    "\\frac{n_2-n_1}{n_2+n_1}\n",
    "\\right)^2,\n",
    "$$\n",
    "with a refractive index of 3.42 for the detector and 1.0 for air, a depletion layer thickness of 5 $\\mu$m, and the spectral absorption coefficient calculated above. \n",
    "\n",
    "The spectral responsivity is calculated using the spectral quantum efficiency and (Eq. 5.5)\n",
    "$$\n",
    "{\\cal R}_{e\\lambda}\n",
    "=\\frac{i_d}{\\Phi_{e}} \n",
    "= \\frac{i_d}{\\frac{hc}{\\lambda}\\Phi_{q}}\n",
    "=\\left\\{\n",
    "\\begin{array}{ll}\n",
    "\\frac{\\eta q\\lambda G}{hc}\n",
    "&{\\rm for\\ \\ } \\lambda \\leq \\lambda_c\\\\\n",
    "0.0\n",
    "&{\\rm for\\ \\ } \\lambda >\\lambda_c\\\\\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "The gain of the photovoltaic detector is 1.0. "
   ]
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  {
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   "source": [
    "# to define detector parameters\n",
    "# wavelength in micrometers, remember to scale down in functions.\n",
    "wavelength = np.linspace(1, 5.5, 200)    \n",
    "\n",
    "a0 = 1.9e4 * 100   # absorption coefficient [m-1], Eq3.5 & 3.6 Dereniak\n",
    "a0p = 800 * 100    # absorption coefficient [m-1] Eq3.5 & 3.6 Dereniak\n",
    "d1 = 0                # start of the detector depletion layer in meter\n",
    "d2 = 5e-6             # end of the detector depletion layer in meter\n",
    "n1 = 1.0              # refraction index of the air\n",
    "n2 = 3.42        # refraction index of the semiconductor being analyzed\n",
    "theta1 = 0.0        # radiation incident angle in radians\n",
    "\n",
    "absorption = rydetector.Absorption(wavelength / 1e6, Eg, tempDet, a0, a0p)\n",
    "quantumEffic = rydetector.QuantumEfficiency(absorption, d1, d2, theta1, n1, n2)\n",
    "responsivity = rydetector.Responsivity(wavelength / 1e6,quantumEffic)"
   ]
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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot absorption coefficient\n",
    "absFig = ryplot.Plotter(1,figsize=(9,4))\n",
    "absFig.semilogY(1,wavelength,absorption,'Absorption Coefficient',\\\n",
    "    r'Wavelength [$\\mu$m]',r'Absorption Coefficient [m$^{-1}$]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XYebMaldoHa1Dgo+krsAvgInAdsARkrYratMXuBD4cERsD3wy775mZmatkeADH4A//SndH+iTn0wjQqNHp0viHYDqR0eN+IwDpkfEjIhYAVwLHFbU5kjgxoh4BSAiXithXzMzs1zGjk1XhL30Epx4IkyZAltvnZ4pNmdOtauzSuuo4DMUmFXweXa2rtDWwGaSGiU9KunoEvY1MzMrybBh6bL36dPh85+Hiy9OI0Df/z4sW1bt6qxSOupZXWpmXfHFhd2A9wH7AT2BByU9lHPf9CXS8cDxAAMGDKCxsbG99VqBpUuXui/LyP1ZXu7P8qnnvjz8cNhrrx5cdtlIzjhjEBdeuJwvfGEG++8/v93PBqvn/tyQdVTwmQ0UPl93GFA8oDgbWBgRy4Blku4Fdsi5LwARcTFwMcCYMWNiwoQJZSm+3jU2NuK+LB/3Z3m5P8vHfZnuDv3AA/CVr2zEWWdty913b8vFF6fL4Uvl/twwddSprkeAd0saKak78Gng5qI2fwD2ktRN0sbArsC0nPuamZmVxR57pHsBXXklzJiR7gZ92mm+B1Bn0SHBJyJWAScCt5PCzO8i4hlJJ0g6IWszDfgz8BQwFbg0Iv7R0r4dUbeZmdWnLl3SXZ+nTYOjjoKzzkqjPvfc0/a+tmHrsPv4RMStEbF1RIyKiB9m6y6KiIsK2vxvRGwXEWMj4tzW9jUzM6u0fv3g8svTE+AjYJ994FvfghUrql2ZtZfv3GxmZtaGffeFJ59MV3/9+Mew++7wwgvVrsraw8HHzMwsh1690uMvbrgh3QNop53g6qurXZWVysHHzMysBB/7GDz1VHr8xaRJcMopsGpVtauyvBx8zMzMSjR0KNx5J5x0EpxzDhx0ECxcWO2qLA8HHzMzs3ZoaIDzzkuTn++/P40ATZtW7aqsLQ4+ZmZm6+GYY+Dee9N9fvbaC6ZOrXZF1hoHHzMzs/U0bly64/Mmm6QrwO66q9oVWUscfMzMzMpg1KgUfrbaCj70IbjnngHVLsmakSv4SDpJUv9KF2NmZlbLNt883d15l13gjDO248Ybq12RFcs74rM/MFPSLZIOl7RRJYsyMzOrVZttBrffDttu+wZHHJGu/rINR67gExEfBkYAtwEnA/MkXSpp7wrWZmZmVpN69YKzznqabbaBj3wEHnyw2hVZk9xzfCJiUUT8IiJ2Az4A7AL8VdJMSd+W1LtiVZqZmdWYPn1WcccdMGQIHHxwuumhVV9Jk5sl7SfpcqARmA8cDUwCdiKNBpmZmVlm0KB0hVfv3ukmh6++Wu2KLO/k5p9Jmg2cDzwHvCciDoyIayLiPuAIUvgxMzOzAiNGwG23wZIlcNhh8Oab1a6ovuUd8ekBfDQito+In0TEOpk1IlYC7y97dWZmZp3A2LEwZQo89hgcdxxEVLui+pU3+JwFTC9cIWkzSUOaPkfEc+UszMzMrDM59FA480z4zW/goouqXU39yht8fg8MK1o3DLiprNWYmZl1YqeeChMnwsknw6OPVrua+pQ3+IyJiKcLV2Sftyl/SWZmZp1Tly5w1VUwcCAceaTn+1RD3uDzmqTRhSuyz4vKX5KZmVnn1a8fXHklvPBCGgGyjpU3+EwGbpB0iKTtJB0KXA9cWrnSzMzMOqd994UvfxkuuAD+8pdqV1NfuuVs92NgJfAzYDgwixR6zqlQXWZmZp3aWWely9z/67/g6aehZ89qV1Qf8j6yYk1E/G9EbBMRvbLXn0XEmkoXaGZm1hn17Jmu7nrxRfjRj6pdTf3IO+KDpDHADsA6j6aIiMnlLsrMzKwe7LMPTJoEP/lJmuy87bbVrqjzy3vn5tOAJ4FTSI+oaFqOqlxpZmZmnd/PfgYbbwxf/3q1K6kPeUd8TgbGRYQfsWZmZlZGAwfCaafBN78Jd9+dJj5b5eS9qust0jO6zMzMrMxOOgm22AK+9jVY49mzFZU3+HwXuEDS5pK6FC6VLM7MzKwe9OiRJjg//jhcd121q+nc8gaXK4AvALNJl7WvBFZlr2ZmZraejjgiTW4+80yP+lRS3uAzMlu2KliaPpuZmdl66tIFvv1t+Mc/4Oabq11N55X3Pj4vR8TLpBsXrmj6nK0zMzOzMjj8cBg1Ko36RFS7ms4p7+XsfSVNAd4GpmfrPizpzEoWZ2ZmVk+6dYNvfSs9uf3uu6tdTeeU91TXRcDrwAhgRbbuQeDwShRlZmZWrz7zGejfH37xi2pX0jnlDT77ASdFxFwgACJiATCwUoWZmZnVox494Ljj4A9/gFdeqXY1nU/e4PM60L9whaQtgLllr8jMzKzOnXBCev3Vr6pbR2eUN/hcCtwgaR+gi6TdgCtJp8DMzMysjEaMgEMPhUsugRUr2m5v+eUNPj8Bfgf8AmgAJgN/AM6rUF1mZmZ17fjjYcEC+POfq11J55L3cvaIiHMjYruI6BUR22affbGdmZlZBRxwQJrkPGVKtSvpXFp8SKmkvSPi3ux9i49MiwhfcGdmZlZmDQ3wqU/B5ZfDkiXQp0+1K+ocWns6+4XA2Oz9ZS20CXz3ZjMzs4o48ki48EL4/e9h0qRqV9M5tBh8ImJswfuRHVOOmZmZNdl9d9hyy3S6y8GnPPLeuXlHScOL1g2XtENlyjIzMzMpPcbizjth8eJqV9M55L2q62rS1VyFugNXlbccMzMzK3TIIbB6Ndx1V7Ur6RzyBp8tImJG4YqIeBHYsuwVmZmZ2X+MHw99+8Jtt1W7ks4hb/CZLWnnwhXZ5znlL8nMzMyadOsGBx6Ygo9vIrP+8gafnwN/kPQlSQdL+hJwE3BO5UozMzMzgIkTYe5cePLJaldS+1q7nP0/IuISSYuBzwPDgVnAKRFxfQVrMzMzM+Cgg9LrbbfBjjtWtZSal3fEh4i4LiIOiojts1eHHjMzsw4weDDsvLPn+ZRDa3dunhQRV2XvP9dSu4iYXInCzMzMbK1994ULLoDly2GjjapdTe1qbcTniIL3k1pYjsr7RZIOkvS8pOmSTm2l3S6SVkv6RKn7mpmZdVbjx6fQ43k+66e1OT4XFbw/MCJWtvdLJHUlPdn9AGA28IikmyPi2Wba/QS4vdR9zczMOrNdd02vDz8M48ZVt5Za1tqIz9UF7xet5/eMA6ZHxIyIWAFcCxzWTLsvATcAr7VjXzMzs05r2DAYMgQeeqjaldS21kZ85kk6EXgW6CZpH0DFjXI+nX0o6UqwJrOBXQsbSBoKfBTYF9illH0LjnE8cDzAgAEDaGxszFGatWXp0qXuyzJyf5aX+7N83JflVYn+HDVqexobe9PY+HBZj1tPWgs+xwKnA18GNgKam8Sc9+ns7whM2b6FzgW+GRGrpXWa59k3rYy4GLgYYMyYMTFhwoQcpVlbGhsbcV+Wj/uzvNyf5eO+LK9K9Ochh8A3vwnbbz+BAQPKeui60VrweTYi9geQND0iRq/H98wm3f+nyTDeedfn9wPXZqGnP3CwpFU59zUzM+v0xo9Pr1Onwoc+VN1aalVrc3xeLng/cz2/5xHg3ZJGSuoOfBq4ubBBRIyMiC0jYkvgeuD/RcTv8+xrZmZWD973Puja1fN81kdrIz5vShoLTAPGKQ3FNDfHZ01bXxIRq7L5QrcDXYHJEfGMpBOy7ReVum9b32lmZtbZ9OoFY8emER9rn9aCz+nAVNL8HoBVRdtFmmvTNc8XRcStwK1F65oNPBFxTFv7mpmZ1aPtt4e//a3aVdSuFk91RcQvgU2AEcBbpEnMhctI8k1sNjMzszIZNQpeeSXdzNBK1+pDSiNiFTBb0k4R8XJrbc3MzKzyRo+GNWtg5kwYM6ba1dSeVh9SKukPABHxz+zz6UXbH6lcaWZmZlZsdHaN9fTp1a2jVrX1dPZ9ij5/qejzNmWsxczMzNrg4LN+2go+xYqv6mr2RoJmZmZWGQMGQJ8+Dj7tVWrwcdAxMzOrIimN+jj4tE+rk5uBBknHsnakZyNJnythfzMzMyuz0aPh8cerXUVtaiu4PAwcXfB5KjCpaLuZmZl1oNGj4aabYNUq6OYhiJK0dTn7hA6qw8zMzHIaPTqFnldega18R72SlDrHx8zMzKrMV3a1n4OPmZlZjXHwaT8HHzMzsxqz+ebQs6eDT3s4+JiZmdUYCYYMgXnzql1J7ck9F1zSxsBooHfh+ojwM2LNzMw6WPfusHJltauoPbmCj6Sjgf8DVpCe1N4kgC0qUJeZmZm1oqHBwac98o74/BT4eETcWclizMzMLB8Hn/bJO8dnBdBYwTrMzMysBA4+7ZM3+HwXOEdS/0oWY2ZmZvk4+LRP3uDzAvBhYL6k1dmyRtLqCtZmZmZmLWhogBUrql1F7ck7x+cq4NfAb1l3crOZmZlVQUMDLFlS7SpqT97g0w/4XkREJYsxMzOzfHw5e/vkPdV1Oes+ld3MzMyqyHN82ifviM844ERJ3wbmF26IiL3LXpWZmZm1ysGnffIGn0uyxczMzDYADj7tkyv4RMSVlS7EzMzM8nPwaZ+8j6z4XEvbImJy+coxMzOzPBx82ifvqa7iic2DgVHAA4CDj5mZWQdz8GmfvKe69ilel40CbVv2iszMzKxNvoFh++S9nL05VwCfL1MdZmZmVgLfx6d98s7xKQ5IGwNHAYvLXZCZmZm1zae62ifvHJ9VQPFdm18Fji9vOWZmZpZHQwOsWZOWLutz/qbO5A0+I4s+L4uIheUuxszMzPJpaEivK1fCRhtVt5ZakjcjnhIRLxcsCwEknVu50szMzKwlhcHH8ssbfI5pYb2f32VmZlYFDj7t0+qproIbF3Zr5iaGWwE+3WVmZlYFDj7t09Ycn6YRne6sO7oTpIeVfrYSRZmZmVnrmoKP7+VTmlaDT9ONCyWdGRHf6ZiSzMzMrC0e8WmfvHdu/g6ApIFA76JtMypQl5mZmbWie/f06uBTmrw3MPwg6ZlcmxdtCqBruYsyMzOz1nnEp33yXtV1IfADoFdEdClYHHrMzMyqwMGnffLewHAz4FcRUXz3ZjMzM6sCB5/2yTvicxlwbCULMTMzs/wcfNon74jPeOAkSacC8wo3RMTeZa/KzMzMWuXg0z55g8+l2WJmZmYbAAef9sl7OfuVlS7EzMzM8vMNDNsn74gPkgYB44D+gJrWR8TkCtRlZmZmrfB9fNon7318PgJcDfwT2B54BhgL3E+6v4+ZmZl1IJ/qap+8V3WdCRwbETsBy7LX44FH836RpIMkPS9pejZJunj7ZyQ9lS1/k7RD3n3NzMzqjYNP++QNPltExHVF664Ejs6zs6SuwC+AicB2wBGStitq9hLwgYh4L+lmiReXsK+ZmVldcfBpn7zB57Vsjg/ATEm7AaPI/7iKccD0iJgRESuAa4HDChtExN8i4t/Zx4eAYXn3NTMzqzcOPu2Td3LzJcCewA3Az4G/AmuAs3PuPxSYVfB5NrBrK+0/D9xW6r6SjiedgmPAgAE0NjbmLM9as3TpUvdlGbk/y8v9WT7uy/KqdH8uWtQd2J1nnnmBxsY5Ffuezibv5ew/KXj/a0mNpOd2Tcv5PWpmXbOPv5C0Dyn47FnqvhFxMdkpsjFjxsSECRNylmetaWxsxH1ZPu7P8nJ/lo/7srwq3Z8LF6bXkSO3ZsKErSv2PZ1N7svZC0XEKyXuMhsYXvB5GPCOeCrpvaQbJU6MiEWl7GtmZlZPfKqrffJezj6LlkdZtshxiEeAd0saCbwKfBo4sug7tgBuBCZFxAul7GtmZlZvmu7j4xsYlibviM9RRZ83B75MmmjcpohYJelE4HbShOjJEfGMpBOy7RcB3wP6ARdKAlgVEe9vad+cdZuZmXVKHvFpn7xzfO4pXpfN8/kzcF7OY9wK3Fq07qKC98cBx+Xd18zMrJ51za6rdvApTd7L2ZuzHBhZrkLMzMwsPymN+jj4lCbvHJ8zilZtDBzM2kvOzczMrIM5+JQu7xyf4UWflwHnAFeVtxwzMzPLy8GndHnn+Bxb6ULMzMysNA4+pWsz+GTPyjoSOBAYACwAGoEpEfFWRaszMzOzFjn4lK7Vyc2SNgEeAH4GrAYez15/BEyVNFjSMEmfqnilZmZmtg4Hn9K1NeJzFrAI2C8iljWtlNQL+B0whfSw0tMqVqGZmZk1q3t338CwVG0Fn48C4wtDD0BELJP0RWAG8LmIuKZSBZqZmVnzPOJTurbu47Mp6TERzZkNLI+IK8pakZmZmeXi4FO6toLPi8C+LWzbD5he3nLMzMwsLwef0rUVfM4Bfi3p45K6AEjqIukTwBXZdjMzM6sCB5/StTrHJyKukNSPFHJ+I2kh0J/0uIozIuLyypdoZmZmzXHwKV2b9/GJiLMlXQzsTgo9C4EHI+KNShdnZmZmLWtogOXLq11Fbcl75+YlwO0VrsXMzMxK0NAAS5dWu4rasj5PZzczM7Mq8n18SufgY2ZmVqM8x6d0Dj5mZmY1ysGndA4+ZmZmNcrBp3QOPmZmZjXKwad0Dj5mZmY1ysGndA4+ZmZmNcrBp3QOPmZmZjXKwad0Dj5mZmY1qnt3B59SOfiYmZnVqIYG38CwVA4+ZmZmNaqhAdasSYvl4+BjZmZWoxoa0qtPd+Xn4GNmZlajHHxK5+BjZmZWoxx8SufgY2ZmVqMcfErn4GNmZlajHHxK5+BjZmZWoxx8SufgY2ZmVqO6d0+vDj75OfiYmZnVqKYRH9/EMD8HHzMzsxrlU12lc/AxMzOrUQ4+pXPwMTMzq1EOPqVz8DEzM6tRDj6lc/AxMzOrUQ4+pXPwMTMzq1EOPqVz8DEzM6tRvo9P6Rx8zMzMapRHfErn4GNmZlajfAPD0jn4mJmZ1SiP+JTOwcfMzKxGOfiUzsHHzMysRjn4lM7Bx8zMrEY5+JTOwcfMzKxGOfiUzsHHzMysRvk+PqXrsOAj6SBJz0uaLunUZrZL0vnZ9qck7Zx3XzMzs3rkEZ/SdUjwkdQV+AUwEdgOOELSdkXNJgLvzpbjgV+WsK+ZmVnd6do1vfo+Pvl166DvGQdMj4gZAJKuBQ4Dni1ocxjw64gI4CFJfSVtDmyZY18zM7O6I6VRnwsvhBtuaN8xDjgAzj23rGVt0Doq+AwFZhV8ng3smqPN0Jz7AiDpeNJoEQMGDKCxsXG9irZk6dKl7ssycn+Wl/uzfNyX5dVR/Tlp0ghefLFXu/dfvnwJjY2z2m7YSXRU8FEz6yJnmzz7ppURFwMXA4wZMyYmTJhQQonWksbGRtyX5eP+LC/3Z/m4L8uro/pz/b9iIDBq/QupER0VfGYDwws+DwPm5GzTPce+ZmZmZm3qqKu6HgHeLWmkpO7Ap4Gbi9rcDBydXd01Hng9Iubm3NfMzMysTR0y4hMRqySdCNwOdAUmR8Qzkk7Itl8E3AocDEwH3gSObW3fjqjbzMzMOpeOOtVFRNxKCjeF6y4qeB/AF/Pua2ZmZlYq37nZzMzM6oaDj5mZmdUNBx8zMzOrGw4+ZmZmVjeU5hR3PpKWAM9Xu45Ooj+wsNpFdCLuz/Jyf5aP+7K83J/lNSYi+qzvQTrsqq4qeD4i3l/tIjoDSX93X5aP+7O83J/l474sL/dneUn6ezmO41NdZmZmVjccfMzMzKxudObgc3G1C+hE3Jfl5f4sL/dn+bgvy8v9WV5l6c9OO7nZzMzMrFhnHvExMzMzW4eDj5mZmdWNmgs+kg6S9Lyk6ZJObWa7JJ2fbX9K0s559603kiZLek3SP1rY7r4sgaThkv4qaZqkZyR9uZk27tMcJPWQNFXSk1lfnt5MG/dliSR1lfS4pFua2eb+LIGkmZKelvREc5dZuz/zk9RX0vWSnsv++7lb0fby9mVE1MwCdAVeBLYCugNPAtsVtTkYuA0QMB54OO++9bYAewM7A/9oYbv7srT+3BzYOXvfB3jBfz7b3ZcCemfvG4CHgfHuy/Xu168CU4Bbmtnm/iytL2cC/VvZ7v7M35dXAsdl77sDfSvZl7U24jMOmB4RMyJiBXAtcFhRm8OAX0fyENBX0uY5960rEXEv8K9WmrgvSxARcyPisez9EmAaMLSomfs0h6x/lmYfG7Kl+EoM92UJJA0DPgRc2kIT92d5uT9zkLQJ6X/CLwOIiBURsbioWVn7staCz1BgVsHn2bzzL5aW2uTZ19blvmwnSVsCO5FGKgq5T3PKTss8AbwG3BkR7sv1cy7wDWBNC9vdn6UJ4A5Jj0o6vpnt7s98tgIWAJdnp2EvldSrqE1Z+7LWgo+aWVf8f4Ettcmzr63LfdkOknoDNwAnR8QbxZub2cV92oyIWB0ROwLDgHGSxhY1cV/mJOkQ4LWIeLS1Zs2sc3+2bI+I2BmYCHxR0t5F292f+XQjTbn4ZUTsBCwDiufqlLUvay34zAaGF3weBszJ2SbPvrYu92WJJDWQQs81EXFjM03cpyXKhr0bgYOKNrkv89sD+LCkmaTTAftKurqojfuzBBExJ3t9DbiJdNqlkPszn9nA7IIR3etJQai4Tdn6staCzyPAuyWNlNQd+DRwc1Gbm4Gjs1ng44HXI2Juzn1tXe7LEkgS6Tz1tIg4p4Vm7tMcJA2Q1Dd73xPYH3iuqJn7MqeI+FZEDIuILUn9cXdEHFXUzP2Zk6Rekvo0vQcOBIqvjnV/5hAR84BZksZkq/YDni1qVta+rKmns0fEKkknAreTZnNPjohnJJ2Qbb8IuJU0A3w68CZwbGv7VuFnbDAk/QaYAPSXNBv4PmkSqfuyffYAJgFPZ3NTAE4DtgD3aYk2B66U1JX0P2i/i4hb/O96ebk/220QcFP6fx26AVMi4s/uz3b7EnBNFl5mAMdWsi/9yAozMzOrG7V2qsvMzMys3Rx8zMzMrG44+JiZmVndcPAxMzOzuuHgY2ZmZnXDwcfMzMzqhoOPmZmZ1Q0HHzOrCkkzJe2/oX+3pJC0TNIPK1TL3ZLelnR/JY5vZuty8DGrM5K+JenWonX/bGHdpzu2uvIrU8DaISK+XZaCikTEvsAJlTi2mb2Tg49Z/bkX2CN7HASSBpMeVbJz0brRWVszs07Dwces/jxCCjo7Zp/3Bv4KPF+07sWImCPpVEkvSloi6VlJH206ULbt+sKDSzpP0vnZ+yGSbpC0QNJLkk5qqajW2majNl+T9JSk1yX9VlKPgu07S3o8q/G6bPuZkq4iPSvtj5KWSvpGwVfu2NLx2iLp25J+WfB5M0krm46R1fv17PjLJF0maZCk27Ia75K0Wd7vM7PycfAxqzMRsQJ4mBRuyF7vA+4vWtc02vMisBewKXA6cLWkzbNtvwEOlrQJQDZi9ClgiqQuwB+BJ4GhpKcunyzpg8U15Wz7KeAgYCTwXuCYbN/uwE3AFcC7spo+mv3WScArwKER0TsiftrW8XJ6D/BEwecdgecj4u2CdR8HDgC2Bg4FbiM9tLY/6b+9LYZAM6scBx+z+nQPa0POXqTgc1/RunsAIuK6iJgTEWsi4rfAP4Fx2baXgceAj2T77Qu8GREPAbsAAyLijIhYEREzgEuA5uYN5Wl7flbHv0ghacds/XjSE7LPj4iVEXEjMDVHH7R0vDyaCz5PFrW5ICLmR8SrpL59OCIej4jlpKC2UwnfZ2Zl0q3aBZhZVdwLfDE73TIgIv4paT5wZbZubNYGSUcDXwW2zPbtTRq1aDIFOAL4NXBk9hlgBDBE0uKCtl1JIaBYnrbzCt6/CQzJ3g8BXo2IKNg+q9lfva6WjteqbIRpFPB0weodWDcIAcwveP9WM5975/k+MysvBx+z+vQg6dTV8cADABHxhqQ52bo5EfGSpBGkkZf9gAcjYrWkJwAVHOs64GxJw0inmHbL1s8CXoqId+eop5S2xeYCQyWpIPwMJ52iA4jmd2u37UhB600ASQImsDbwmdkGzKe6zOpQRLwF/J00klM4qnJ/tq5pfk8vUnBYACDpWNJoUOGxFgCNwOWk8DIt2zQVeEPSNyX1lNRV0lhJuzRTUiltiz0IrAZOlNRN0mFkp+Iy84Gtchwnr/cAAyWNktQT+AFpxGpmGb/DzCrEwcesft0DDCSFnSb3ZevuBYiIZ4GzSeFiPukv/QeaOdYUYH8KRj0iYjVpUu+OwEvAQuBS0kjTOkpp28y+K4CPAZ8HFgNHAbcAy7MmZwHfkbRY0tfaOl4O7wFuJ01Wnk7qlxlARe7zY2blpXVPi5uZ1T5JDwMXRcTlZTjW26QQdX5EfFfSbcClEXHD+h47O/6dpAnaUyNiv3Ic08xa5jk+ZlbzJH2AdB+ihcBnSJen/7kcx46I4vv7vAeY1lzbdh7/gHIdy8za5uBjZp3BGOB3pCulXgQ+ERFzy/0l2RVvA0mX9JtZDfKpLjMzM6sbntxsZmZmdcPBx8zMzOqGg4+ZmZnVDQcfMzMzqxsOPmZmZlY3HHzMzMysbjj4mJmZWd34/+OAh6VpSHEHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot quantum efficiency\n",
    "QE = ryplot.Plotter(1,figsize=(9,4))\n",
    "QE.plot(1,wavelength,quantumEffic,'Spectral Quantum Efficiency',\\\n",
    "    r'Wavelength [$\\mu$m]','Quantum Efficiency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot spectral response\n",
    "Respons = ryplot.Plotter(1,figsize=(9,4))\n",
    "Respons.plot(1,wavelength,responsivity,'Spectral Responsivity',\\\n",
    "    r'Wavelength [$\\mu$m]','Responsivity [A/W]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the spectral photon and radiant irradiance on the detector for a test source blackbody source with unity emissivity and temperature $T_s = 2000$ K.  The source area $A_s = 33$ mm$^2$, located a distance $R = 100$ mm from the detector. The irradiance is calculated as \n",
    "$$\n",
    "E_{s\\lambda}=\\epsilon_{\\lambda} M_\\lambda(T_s)\\omega/\\pi=M(T_s)A_s/(\\pi R^2),\n",
    "$$\n",
    "where the spectral exitance is given by (Eq. 3.1 and 3.3) for radiant and photon exitance, respectively.\n",
    "$$\n",
    "M_{e\\lambda}(T)\n",
    "=\\frac{2\\pi h c^2}{\\lambda^5 \\left(e^{hc/(\\lambda kT)}-1\\right)} \n",
    "=\\frac{c_{1e\\lambda}}{\\lambda^5 \\left(e^{c_{2\\lambda}/(\\lambda T)}-1\\right)}\n",
    "$$\n",
    "and\n",
    "$$\n",
    "M_{q\\lambda}(T)\n",
    "=\\frac{2\\pi c}{\\lambda^4 \\left(e^{hc/(\\lambda kT)}-1\\right)}\n",
    "=\\frac{c_{1q\\lambda}}{\\lambda^4 \\left(e^{c_{2\\lambda}/(\\lambda T)}-1\\right)}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source temperature             = 2000 K\n",
      "Source emissivity              = 1.0 \n",
      "Source distance                = 0.1 m\n",
      "Source area                    = 3.3e-05 m2\n",
      "Source solid angle             = 0.0032999999999999995 sr\n"
     ]
    }
   ],
   "source": [
    "# to prepare for detector signal current calculation\n",
    "tempSource = 2000       # source temperature in K\n",
    "emisSource = 1.0          # source emissivity\n",
    "distance = 0.1      # distance between source and detector\n",
    "areaSource = 0.000033     # source area in m2\n",
    "solidAngSource = areaSource / (distance ** 2)\n",
    "print (\"Source temperature             = {0} K\".format(tempSource))\n",
    "print (\"Source emissivity              = {0} \".format(emisSource))\n",
    "print (\"Source distance                = {0} m\".format(distance))\n",
    "print (\"Source area                    = {0} m2\".format(areaSource))\n",
    "print (\"Source solid angle             = {0} sr\".format(solidAngSource))\n",
    "EsourceQL =(emisSource * ryplanck.planck(wavelength,tempSource,'ql') \\\n",
    "    * solidAngSource) / (np.pi )\n",
    "EsourceEL = (emisSource * ryplanck.planck(wavelength, tempSource,'el')*\\\n",
    "    solidAngSource) / (np.pi )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the spectral photon and radiant irradiance on the detector for a hemispherical background blackbody source with unity emissivity and temperature of 280 K.  The calculation is done as above but for an hemispherical solid angle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background temperature         = 280.0 K\n",
      "Background emissivity          = 1.0 \n",
      "Background solid angle         = 3.141592653589793 sr\n"
     ]
    }
   ],
   "source": [
    "# to prepare for detector background current calculation \n",
    "tempBkg = 280.0              # background temperature in K\n",
    "emisBkg = 1.0                # background emissivity\n",
    "solidAngBkg = np.pi       # background is hemisphere\n",
    "\n",
    "print (\"Background temperature         = {0} K\".format(tempBkg))\n",
    "print (\"Background emissivity          = {0} \".format(emisBkg))\n",
    "print (\"Background solid angle         = {0} sr\".format(solidAngBkg))\n",
    "\n",
    "#spectral irradiance for test setup, for both source and background\n",
    "# in units of photon rate q/(s.m2)\n",
    "EbkgQL = (emisBkg * ryplanck.planck(wavelength, tempBkg, 'ql') * \\\n",
    "    solidAngBkg ) / (np.pi )\n",
    "#in radiant units W/m2\n",
    "EbkgEL = (emisBkg * ryplanck.planck(wavelength, tempBkg, 'el') * \\\n",
    "    solidAngBkg) / (np.pi )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the current through the detector for source and background irradiance using a simplified version of (Eq. 6.17):\n",
    "$$\n",
    " i_{\\cal R}=\n",
    "A_{d} \\widehat{\\cal R}\n",
    "\\int_{0}^{\\infty} E_{s\\lambda}{\\cal R}_\\lambda\\,\n",
    "d\\lambda,\n",
    "$$\n",
    "using the spectral responsivity calculated  above.  The following calculation is performed for both the radiant irradiance and photon irradiance, and should yield the same resulting current.\n",
    "\n",
    "The detector area is 200 $\\mu$m by 200 $\\mu$m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Detector current source        = 4.120e-05 A and 4.120e-05 A\n",
      "Detector current background    = 3.588e-07 A and 3.588e-07 A\n",
      "Detector current total         = 4.155e-05 A and 4.155e-05 A\n"
     ]
    }
   ],
   "source": [
    "# to calculate the detector currents\n",
    "import scipy.constants as const\n",
    "areaDet = (200e-6) ** 2   # detector area in m2\n",
    "\n",
    "iSourceE = np.trapz(EsourceEL * areaDet * responsivity, wavelength)\n",
    "iBkgE = np.trapz(EbkgEL * areaDet * responsivity, wavelength)\n",
    "iSourceQ = np.trapz(EsourceQL * areaDet * quantumEffic * const.e,wavelength)\n",
    "iBkgQ = np.trapz(EbkgQL * areaDet * quantumEffic * const.e, wavelength)\n",
    "iTotalE = iSourceE + iBkgE\n",
    "iTotalQ = iSourceQ + iBkgQ\n",
    "\n",
    "print (\"\\nDetector current source        = {0:.3e} A and {1:.3e} A\".\\\n",
    "    format(iSourceQ,iSourceE))\n",
    "print (\"Detector current background    = {0:.3e} A and {1:.3e} A\".\\\n",
    "    format(iBkgQ,iBkgE))\n",
    "print (\"Detector current total         = {0:.3e} A and {1:.3e} A\".\\\n",
    "    format(iTotalQ,iTotalE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the detector reverse saturation current $I_{\\rm sat}$. First, calculate the carrier diffusion lengths $L_e$ and $L_h$ by using (Eq. 5.125 and 5.126)\n",
    "\n",
    "$$\n",
    "L_h = \\sqrt{kT\\mu_h \\tau_h/q}\n",
    "$$\n",
    "and\n",
    "$$\n",
    "L_e = \\sqrt{kT\\mu_e \\tau_e/q}\n",
    "$$\n",
    "Second, calculate the intrinsic carrier concentration $n_i$ using (Eq. 5.74)\n",
    "$$\n",
    "n_i^2 = n p = 4 \\left(\\frac{2\\pi k T}{h^2}\\right)^3 \\exp\\left[\\frac{-E_g}{kT}\\right]\\left(m^*_h m^*_e\\right)^{3/2}\n",
    "$$\n",
    "The donor concentration is calculated using (Eq. 5.76 and 5.77)\n",
    "$$\n",
    "p_n = n^2_i/n_n = n^2_i/n_d.\n",
    "$$\n",
    "and \n",
    "$$\n",
    "n_p = n^2_i/p_p = n^2_i/n_a.\n",
    "$$\n",
    "to determine the concentration of carriers.\n",
    "Finally, calculate the reverse-saturation current with (Eq. 5.131)\n",
    "$$\n",
    "I_{\\rm sat}=q A_d \\left(\n",
    "\\frac{n_i^2 L_e}{n_a \\tau_e} +    \\frac{n_i^2 L_h}{n_d \\tau_h}\n",
    "\\right)\n",
    "$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values used in this calculation for the InSb detector are as follows:\n",
    "electron mobility 100.0 cm$^2$/(V$\\cdot$s);\n",
    "electron lifetime  $1\\times10^{-8}$ s;\n",
    "hole mobility  1.0  cm$^2$/(V$\\cdot$s);\n",
    "hole lifetime  $1\\times10^{-8}$ s;\n",
    "electron effective mass 0.014 of electron mass;\n",
    "hole effective mass 0.43 of electron mass;\n",
    "acceptor concentration $1\\times10^{16}$ m$^{-3}$;\n",
    "donor concentration $1\\times10^{16}$ m$^{-3}$;\n",
    "$E_g$ as calculated  above; and \n",
    "detector temperature of 80 K. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I0               = 6.480223667228038e-08 [A] \n",
      "ni               = 3327344312803649.5 [m-3] \n"
     ]
    }
   ],
   "source": [
    "# to calculate the reverse saturation current\n",
    "eMob = 100.0    # electron mobility - m2/V.s\n",
    "hMob = 1.0    # hole mobility - m2/V.s\n",
    "tauE = 1e-8    # electron lifetime - s\n",
    "tauH = 1e-8    # electron lifetime - s\n",
    "me = 0.014 * const.m_e    # electron effective mass\n",
    "mh = 0.43 * const.m_e     # hole effective mass\n",
    "na = 1e16        # doping = acceptor concentration - m-3\n",
    "nd = 1e16        # doping = donor concentration - m-3\n",
    "Isat,ni = rydetector.Isaturation(eMob, tauE, hMob, tauH, me, \n",
    "                                 mh, na, nd, Eg, tempDet, areaDet)\n",
    "print (\"I0               = {0} [A] \".format(Isat))\n",
    "print (\"ni               = {0} [m-3] \".format(ni))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the current-voltage relationship using (Eq 5.128)\n",
    "$$\n",
    "I=\n",
    "I_{\\rm sat}\n",
    "e^{qV/(kT\\beta)}\n",
    "-\n",
    "I_{\\rm sat}\n",
    " -I_{ph},\n",
    "$$\n",
    "\n",
    "The saturation current $I_{\\rm sat}$ is calculated as explained  above.  The photocurrent is determined from (Eq. 5.4 and 5.5)\n",
    "\n",
    "$$\n",
    "{\\cal R}_{q\\lambda}=\\frac{i_d}{\\Phi_{q}} \n",
    "=\\left\\{\\begin{array}{ll}\n",
    "\\eta q G\n",
    "&{\\rm for\\ \\ } \\lambda \\leq \\lambda_c\\\\\n",
    "0.0\n",
    "&{\\rm for\\ \\ } \\lambda >\\lambda_c\\\\\n",
    "\\end{array},\n",
    "\\right. \n",
    "$$\n",
    "and \n",
    "$$\n",
    "{\\cal R}_{e\\lambda}\n",
    "=\\frac{i_d}{\\Phi_{e}} \n",
    "= \\frac{i_d}{\\frac{hc}{\\lambda}\\Phi_{q}}\n",
    "=\\left\\{\n",
    "\\begin{array}{ll}\n",
    "\\frac{\\eta q\\lambda G}{hc}\n",
    "&{\\rm for\\ \\ } \\lambda \\leq \\lambda_c\\\\\n",
    "0.0\n",
    "&{\\rm for\\ \\ } \\lambda >\\lambda_c\\\\\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n",
    "\n",
    "The value of $\\beta$ is determined as explained in (Sec. 5.9.2).  The value for $\\beta=1.7$ is used here because InSb operation is dominated by g-r current (for references see my book).\n",
    "\n",
    "The current-voltage relationship can be calculated for different background flux values, such as 'dark' ($I_{ph}=0$), or for different irradiance values, such as those calculated above.\n",
    "\n",
    "The following figure shows the current-voltage relationship for three cases: (a) the dark current (i.e., no background flux), (b) hemispherical background flux at 280 K, and (c) the test source flux.  \n",
    "\n",
    "Note that the y axis is logarithmic to show detail: *negative values are shown positive because the logarithm of a negative number is not defined*.\n",
    "\n",
    "Note that the dark current I-V curve is close to the background current I-V curve, while the test source curve is shifted down by almost two orders of magnitude; this is caused by the photon current resulting from the test source flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot the IV curves\n",
    "IVbeta = 1.7     # 1 = dominanr diffusion current, 2 = g-r dominant\n",
    "Vbias = np.linspace(-200e-3, 100e-3, 200) # bias voltage range\n",
    "ixvDark = rydetector.IXV(Vbias, IVbeta, tempDet, 0, Isat)\n",
    "ixvBackgnd = rydetector.IXV(Vbias, IVbeta, tempDet, iBkgE, Isat)\n",
    "ixvTotalE = rydetector.IXV(Vbias, IVbeta, tempDet, iTotalE, Isat)\n",
    "IXVplot = ryplot.Plotter(1,2,1,figsize=(9,8))\n",
    "IXVplot.semilogY(1,Vbias,np.abs(ixvDark) * 1e6, plotCol=['g'],label=['Dark'])\n",
    "IXVplot.semilogY(1,Vbias,np.abs(ixvBackgnd) * 1e6, plotCol=['r'],label=['Background'])\n",
    "IXVplot.semilogY(1,Vbias,np.abs(ixvTotalE) * 1e6,'IxV Curve',\\\n",
    "    'Voltage [V]','|Current| [uA]',plotCol=['b'],label=['Test source'])\n",
    "IXVplot.plot(2,Vbias,ixvDark * 1e6, plotCol=['g'],label=['Dark'])\n",
    "IXVplot.plot(2,Vbias,ixvBackgnd * 1e6, plotCol=['r'],label=['Background'])\n",
    "IXVplot.plot(2,Vbias,ixvTotalE * 1e6,'IxV Curve',\\\n",
    "    'Voltage [V]','|Current| [uA]',plotCol=['b'],label=['Test source']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Eq. 5.140) is used to calculate the detector's dynamic resistance under zero-bias voltage, \n",
    "$$\n",
    "R_0=-\\left.\\left(\\frac{dI}{dV}\\right)^{-1}\\right\\vert_{V=0} = \\frac{\\beta k T}{q I_{\\rm sat}}\n",
    "$$\n",
    " using the reverse-saturation current calculated  above.\n",
    " \n",
    "The Johnson noise is calculated using (Eq 5.141) \n",
    "$$\n",
    "i_n^2 \n",
    "= \\frac{4kT}{R_0}\n",
    "=\\frac{4q I_{\\rm sat}}{\\beta }\n",
    "=\\frac{2(2q I_{\\rm sat})}{\\beta },\n",
    "$$\n",
    "\n",
    "The noise PSD for the three current components: (a) diffusion, (b) thermal excitation, and (c) photocurrent is calculated using (Eq. 5.143).\n",
    "$$\n",
    "i^2_n=2q  I_{\\rm sat} e^{qV/(kT\\beta)}  + 2q  I_{\\rm sat}  + 2q   I_{ph}\n",
    "$$\n",
    "\n",
    "The total noise is found by adding all four components in quadrature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R0=              = 180851.36930976599 Ohm\n",
      "NBW=             = 100.0 Hz\n",
      "iNoiseTotal      = 4.253495459385366e-12 A\n",
      "iNoise Johnson=  = 1.5629883099729602e-12 A\n",
      "iNoise shot      = 3.955918498389444e-12 A\n"
     ]
    }
   ],
   "source": [
    "# to calculate R0A product\n",
    "deltaFreq = 100.0      # measurement or desirable bandwidth - Hertz\n",
    "iNoisePsd, R0, johnsonPsd, shotPsd = rydetector.Noise(tempDet, IVbeta, Isat, iBkgE)\n",
    "iNoise = iNoisePsd * np.sqrt(deltaFreq)\n",
    "\n",
    "print('R0=              = {0} Ohm'.format(R0))\n",
    "print('NBW=             = {0} Hz'.format(deltaFreq))\n",
    "print(\"iNoiseTotal      = {0} A\".format(iNoise))\n",
    "print(\"iNoise Johnson=  = {0} A\".format(johnsonPsd * np.sqrt(deltaFreq)))\n",
    "print(\"iNoise shot      = {0} A\".format(shotPsd * np.sqrt(deltaFreq)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NEP is determined from  (Eq. 5.25)\n",
    "$$\n",
    "{NEP}_\\lambda=\\frac{i_d \\sqrt{\\Delta{}f}}{{\\cal R}_\\lambda}\n",
    "$$\n",
    "with the spectral responsivity calculated and the noise current calculated above.  The NEP is calculated and shown next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to calculate NEP\n",
    "NEPower, detectivity = rydetector.NEP(iNoise, responsivity)\n",
    "NEPfig = ryplot.Plotter(1,figsize=(9,4))\n",
    "NEPfig.plot(1,wavelength,NEPower,'Spectral Noise Equivalent Power',\\\n",
    "    r'Wavelength [$\\mu$m]','NEP [W]',\\\n",
    "    pltaxis=[np.min(wavelength), np.max(wavelength), 0,NEPower[0]]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The $D^\\ast$ is calculated from the area of the detector, the noise bandwidth, the noise, and the responsivity, by (Eq. 5.26)\n",
    "$$\n",
    "D^\\ast_\\lambda\n",
    "=\\frac{\\sqrt{\\Delta{}f A_d}}{{NEP}_\\lambda}\n",
    "=\\frac{{\\cal R}_\\lambda \\sqrt{\\Delta{}f A_d} }{i_d \\sqrt{\\Delta{}f}  }\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to calculate Dstar\n",
    "#use either of the following two forms:\n",
    "#dStar = rydetector.DStar(areaDet, deltaFreq, iNoise, responsivity) # units in cm\n",
    "dStar = rydetector.DStar(areaDet, 1, iNoisePsd, responsivity) # units in cm\n",
    "\n",
    "DStarfig = ryplot.Plotter(1,figsize=(9,4))\n",
    "DStarfig.plot(1,wavelength,dStar,'Spectral normalized detectivity',\\\n",
    "    r'Wavelength [$\\mu$m]',r'D* [cm$\\sqrt{{\\rm Hz}}$/W]');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python and module versions, and dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Software versions\n",
      "Python:   3.8.3 64bit [MSC v.1916 64 bit (AMD64)]\n",
      "IPython:   7.30.1\n",
      "OS:   Windows 10 10.0.19041 SP0\n",
      "matplotlib:   3.5.1\n",
      "numpy:   1.20.3\n",
      "pyradi:   1.1.4\n",
      "scipy:   1.7.3\n",
      "pandas:   1.3.5\n",
      "Mon Jan 10 16:55:04 2022 South Africa Standard Time\n"
     ]
    }
   ],
   "source": [
    "# to plot software versions\n",
    "# https://github.com/rasbt/watermark\n",
    "try:\n",
    "    import pyradi.ryutils as ryutils\n",
    "    print(ryutils.VersionInformation('matplotlib,numpy,pyradi,scipy,pandas'))\n",
    "except:\n",
    "    print(\"pyradi.ryutils not found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}