path: root/Basic_Electronics_and_Linear_Circuits/ch5.ipynb

{
 "metadata": {
  "name": "Ch5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 5: Transistors"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.1 Page No 134"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.1\n",
      "#Program to Calculate Collector and Base Currents\n",
      "\n",
      "#Given Circuit Data\n",
      "alpha=0.98                       #alpha(dc),  current gain\n",
      "Ico=1*10**(-6)               #Ampere, collector leakage current\n",
      "Ie=1*10**(-3)                 # Ampere, emitter current\n",
      "\n",
      "#Calculation\n",
      "Ic=alpha*Ie+Ico             #Collector Current\n",
      "Ib=Ie-Ic                          #Base Current \n",
      "#result\n",
      "print \" The Collector Current is Ic=  \",Ic*1000,\"mA\"\n",
      "print \" The Base Current is Ib= \",Ib*10**6,\"microA\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Collector Current is Ic=   0.981 mA\n",
        " The Base Current is Ib=  19.0 microA\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.2 Page No 141"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.2\n",
      "#Program to Determine Dynamic Input Resistance of the Transistor at #the point: Ie=0.5 mA and Vcb= -10 V.\n",
      "\n",
      "#From the Input Characteristics \n",
      "dIe=(0.7-0.3)*10**(-3)                        #A, change in emitter current\n",
      "dVeb=(0.7-0.62)                                  #V, change in emitter base voltage\n",
      "#Calculation\n",
      "ri=dVeb/dIe                                      #Dynamic Input Resistance at Vcb= -10 V \n",
      "#Result\n",
      "print \" The Dynamic Input Resistance is ri=  \",ri,\"ohm\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Dynamic Input Resistance is ri=   200.0 ohm\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.3 Page No 144"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.3\n",
      "# Determine Short Circuit Current Gain of the Transistor\n",
      "\n",
      "#Given Data \n",
      "dIe=1*10**(-3)                  #A, change in emitter current\n",
      "dIc=0.99*10**(-3)             #A, change in the collector current\n",
      "\n",
      "#Calculation\n",
      "hfb=dIc/dIe                       #Short Circuit Current Gain\n",
      "#Result\n",
      "print \"The Short Circuit Current Gain is alpha or hfb= \",hfb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.4 Page No 147 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.4(a)\n",
      "# Determine Common Base Short Circuit Current Gain (alpha)\n",
      "#of the Transistor\n",
      "\n",
      "#Given Data \n",
      "dIe=1*10**(-3)                           #A, change in emitter current\n",
      "dIc=0.995*10**(-3)                    #A, change in collector current\n",
      "\n",
      "#Calculation\n",
      "alpha=dIc/dIe                              #Common Base Short Circuit Current Gain\n",
      "#Result\n",
      "print \" The Common Base Short Circuit Current Gain is alpha= \",alpha\n",
      "\n",
      "#(b)\n",
      "beeta=alpha/(1-alpha)                  #Common Emitter Short Circuit Current Gain\n",
      "# Result\n",
      "print \" The Common Emitter Short Circuit Current Gain is beeta= \",beeta\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Common Base Short Circuit Current Gain is alpha=  0.995\n",
        " The Common Emitter Short Circuit Current Gain is beeta=  199.0\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.5 Page No 147 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.5\n",
      "# Determine DC Current Gain in Common Base Configuration\n",
      "\n",
      "#Given Data \n",
      "Beeta=100.0                            #dc current gain\n",
      "\n",
      "#Calculation\n",
      "Alpha=Beeta/(Beeta+1)    #DC Current Gain in Common Base Configuration\n",
      "# Result\n",
      "print \" The DC Current Gain in Common Base Configuration is Alpha= \",round(Alpha,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The DC Current Gain in Common Base Configuration is Alpha=  0.99\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.6 Page No 150"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.6\n",
      "#Refer Figure 5.20 in the Textbook\n",
      "# Determine the Dynamic Output Resistance, \n",
      "#DC Current Gain & AC Current Gain from given output characteristics\n",
      "\n",
      "#Given Data \n",
      "Vce=10                                       #V, collector emitter voltage\n",
      "Ib=30*10**(-6)                        #A, base current\n",
      "\n",
      "#Calculation from Given Output Characteristics at Ib = 30uA\n",
      "dVce=(12.5-7.5)                      #V, change in collector emitter voltage\n",
      "dic=(3.7-3.5)*10**(-3)           #A, change in collector current\n",
      "Ic=3.6*10**(-3)                      #A, collector current at operating point \n",
      "ro=dVce/dic                            # Dynamic Output Resistance\n",
      "Beeta_dc=Ic/Ib                      # DC Current Gain\n",
      "Beeta_ac=((4.7-3.6)*10**(-3))/((40-30)*10**(-6))                #AC Current Gain, From Graph, Bac=delta(ic)/delta(ib) for given Vce\n",
      "\n",
      "# Result\n",
      "print \"Dynamic Output Resistance ,ro = \",ro/10**(3),\"kohm\"\n",
      "print \" DC Current Gain ,Bdc = \",Beeta_dc\n",
      "print \" AC Current Gain ,Bac = \",Beeta_ac"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dynamic Output Resistance ,ro =  25.0 kohm\n",
        " DC Current Gain ,Bdc =  120.0\n",
        " AC Current Gain ,Bac =  110.0\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.7 Page No 159"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.7\n",
      "#Refer Figure 5.27 in the Textbook\n",
      "# Determine the Q point from given collector characteristics in fig. 5.27\n",
      "\n",
      "#Given Data \n",
      "Vcc=12                                #V, collector bias juncyion voltage\n",
      "Rc=1000.0                           #Ohms, collector resistance\n",
      "Vbb=10.7                            #V.  base bias junction voltage\n",
      "Rb=200000.0                      #Ohms, base resistance\n",
      "Vbe=0.7                              #V, base emitter voltage\n",
      "\n",
      "#Calculation\n",
      "Ib=(Vbb-Vbe)/Rb             # base current\n",
      "#Value of Ib comes out to be 50uA. A dotted Curve is drawn for \n",
      "#Ib=40uA and Ib=60uA.   At the Point of Intersection:\n",
      "Vce=6                                 #V, collector emitter voltage\n",
      "Ic=6*10**(-3)                   #A, collector current\n",
      "# Result\n",
      "print \" Q point:  Ib = \",Ib/10**(-6),\"microA\"\n",
      "print \" Vce = \",Vce,\"V\"\n",
      "print \" Ic =  \",Ic/10**(-3),\"mA\"\n",
      "#Plot\n",
      "\n",
      "#DC Load LIne  AT Ib=50 microA\n",
      "Vce1=[0,12]\n",
      "Ic1=[12,0]\n",
      "a1=plot(Vce1,Ic1)\n",
      "xlim(0,14)\n",
      "ylim(0,17)\n",
      "\n",
      "\n",
      "# AT Ib=20 microA\n",
      "Vce2=[0,1,12]\n",
      "Ic2=[0,1.5,3]\n",
      "a2=plot(Vce2,Ic2)\n",
      "\n",
      "# AT Ib=40 microA\n",
      "Vce3=[0,1,12]\n",
      "Ic3=[0,4,5]\n",
      "a3=plot(Vce3,Ic3)\n",
      "\n",
      "#At IB=50\n",
      "Vcex=[3.2,9]\n",
      "Icx=[5.5,6]\n",
      "ax=plot(Vcex,Icx,linestyle='--')\n",
      "qx=plot(6.1,5.9,marker='o',label='$Q  point$')\n",
      "legend()\n",
      "\n",
      "# AT Ib=60 microA\n",
      "Vce4=[0,1,12]\n",
      "Ic4=[0,6.5,8]\n",
      "a4=plot(Vce4,Ic4)\n",
      "\n",
      "# AT Ib=80 microA\n",
      "Vce5=[0,1,12]\n",
      "Ic5=[0,9,10]\n",
      "a5=plot(Vce5,Ic5)\n",
      "\n",
      "# AT Ib=100 microA\n",
      "Vce6=[0,1,12]\n",
      "Ic6=[0,12,12.5]\n",
      "a6=plot(Vce6,Ic6)\n",
      "\n",
      "# AT Ib=120 microA\n",
      "Vce7=[0,1,12]\n",
      "Ic7=[0,14.2,15]\n",
      "a7=plot(Vce7,Ic7)\n",
      "xlabel(\"$Vce(volt)$\")\n",
      "ylabel(\"$Ic(mA)$\")\n",
      "show(a1)\n",
      "show(ax)\n",
      "show(qx)\n",
      "show(a2)\n",
      "show(a3)\n",
      "show(a4)\n",
      "show(a5)\n",
      "show(a6)\n",
      "show(a7)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Q point:  Ib =  50.0 microA\n",
        " Vce =  6 V\n",
        " Ic =   6.0 mA\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEOCAYAAACNY7BQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI4lBCCGEGUkMQgghzPx/TjiQ+AqbBlQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 5.8 Page No 173"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Example 5.8\n",
      "#Program to Calculate Dynamic Drain Resistance of JFET\n",
      "\n",
      "#Given Data \n",
      "u=80.0                                      # Amplification Factor\n",
      "gm=200*10**(-6)                   # S, Transconductance\n",
      "\n",
      "#Calculation\n",
      "rd=u/gm                                #Dynamic Drain Resistance\n",
      "# Result\n",
      "print \" The Dynamic Drain Resistance of JFET is rd= \",rd/10**(3),\"kohm\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Dynamic Drain Resistance of JFET is rd=  400.0 kohm\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}