Complementary BJT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e2277a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sympy as sp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import EngFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5411001",
   "metadata": {},
   "outputs": [],
   "source": [
    "IB, Gm, ICn, ICp, Bn, Bp, VT = sp.symbols(r'I_B G_m I_{Cn} I_{Cp} \\beta_n \\beta_p V_T')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9d8abc",
   "metadata": {},
   "source": [
    "$$\n",
    "I_B = \\frac{I_{Cn}}{\\beta_n} - \\frac{I_{Cp}}{\\beta_p}\\\\\n",
    "G_m = \\frac{I_{Cn}}{V_T} + \\frac{I_{Cp}}{V_T}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26da62c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{1}{\\beta_{n}} & - \\frac{1}{\\beta_{p}}\\\\\\frac{1}{V_{T}} & \\frac{1}{V_{T}}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1/\\beta_n, -1/\\beta_p],\n",
       "[    1/V_T,      1/V_T]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = sp.Matrix([\n",
    "    [1/Bn, -1/Bp],\n",
    "    [1/VT, 1/VT]\n",
    "])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6722193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}I_{B}\\\\G_{m}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[I_B],\n",
       "[G_m]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = sp.Matrix([\n",
    "    IB,\n",
    "    Gm\n",
    "])\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0dd28b",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "I_B \\\\\n",
    "G_m\n",
    "\\end{bmatrix}\n",
    "&=\n",
    "\\begin{bmatrix}\n",
    "\\frac{1}{\\beta_{n}} & - \\frac{1}{\\beta_{p}}\\\\\n",
    "\\frac{1}{V_{T}} & \\frac{1}{V_{T}}\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "I_{Cn} \\\\\n",
    "I_{Cp}\n",
    "\\end{bmatrix}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26d03a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{G_{m}}{\\frac{1}{V_{T}} + \\frac{\\beta_{p}}{V_{T} \\beta_{n}}} + \\frac{I_{B}}{\\frac{1}{\\beta_{p}} + \\frac{1}{\\beta_{n}}}\\\\\\frac{G_{m} V_{T} \\beta_{p}}{\\beta_{n} + \\beta_{p}} - \\frac{I_{B} \\beta_{n} \\beta_{p}}{\\beta_{n} + \\beta_{p}}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[            G_m/(1/V_T + \\beta_p/(V_T*\\beta_n)) + I_B/(1/\\beta_p + 1/\\beta_n)],\n",
       "[G_m*V_T*\\beta_p/(\\beta_n + \\beta_p) - I_B*\\beta_n*\\beta_p/(\\beta_n + \\beta_p)]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = A.inv()*b\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8cce0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{400 I_{B}}{9} + 2.88888888888889 \\cdot 10^{-5}\\\\2.31111111111111 \\cdot 10^{-5} - \\frac{400 I_{B}}{9}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[400*I_B/9 + 2.88888888888889e-5],\n",
       "[2.31111111111111e-5 - 400*I_B/9]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x.subs({\n",
    "    Gm: 2e-3,\n",
    "    VT: 26e-3,\n",
    "    Bn: 100,\n",
    "    Bp: 80,\n",
    "})\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cf2df4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = sp.lambdify(IB, X)\n",
    "ib_arr = np.linspace(-250e-9, 250e-9)\n",
    "collector_currents = fn(ib_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0abdf819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ib_arr, collector_currents[0][0], label='NPN')\n",
    "plt.plot(ib_arr, collector_currents[1][0], label='PNP')\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.grid()\n",
    "ax.set_xlabel('Net base current')\n",
    "ax.set_ylabel('Collector current')\n",
    "ax.xaxis.set_major_formatter(EngFormatter(unit='A'))\n",
    "ax.yaxis.set_major_formatter(EngFormatter(unit='A'))\n",
    "ax.legend(loc='lower left');\n",
    "ax.set_title(r'$\\beta_n=100$'+'\\n'+r'$\\beta_p=80$'+'\\n'+r'$G_m=2\\mathrm{mS}$');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c16b6f6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}