PowerSwitchingDevices_MOSFET_TransferCurve.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bingsen-wang/PowerElectronics/blob/main/PowerSwitchingDevices_MOSFET_TransferCurve.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAMKoELNpsK-"
      },
      "source": [
        "<center><h1><b>Transfer Curve of Power MOSFET\n",
        "</h1></b>\n",
        "\n",
        "Dr. Bingsen Wang\n",
        "\n",
        "1/1/2024\n",
        "</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transfer Conductance"
      ],
      "metadata": {
        "id": "x8hvmZGToTH7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In active region, the drain current $i_D$ is independent from the drain-source voltage and only depends on the gate-source voltage.\n",
        "$$ i_D =  K(v_{GS}-V_{th})^2 \\tag{1}$$\n",
        "where $K$ is a constant determined by the physical parameters of the device.\n",
        "\n",
        "The slope of the $i_D$-$v_{GS}$ curve is the **transfer conductance** $g_m$\n",
        "$$g_m = \\frac{\\partial i_D}{\\partial v_{GS}}\\tag{2}$$"
      ],
      "metadata": {
        "id": "NctVo6vN2dn5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hv0YxDrWU4h8"
      },
      "source": [
        "#Python Code\n",
        "The Python code illustrates the transfer conductance power MOSFETs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "XeGsHYTt8IXU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "outputId": "a69b9b73-742f-4a62-9a7c-2cda7b431214"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib\n",
        "from os import linesep\n",
        "from numpy import linspace,heaviside\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import animation,rc\n",
        "rc('animation', html='jshtml')\n",
        "plt.rcParams.update({\"font.family\" : \"sans serif\",\"mathtext.fontset\" : \"cm\"})\n",
        "\n",
        "def iD_transfer(vGS,Vth,K): #ohmic and active regions\n",
        "  return K*(vGS - Vth)**2*heaviside(vGS-Vth,0)\n",
        "\n",
        "def gm(vGS,Vth,K):\n",
        "  vGS_tan = linspace(vGS-3,vGS+3,2)\n",
        "  iD_tan = (vGS_tan - vGS)*2*K*(vGS-Vth) +  K*(vGS - Vth)**2\n",
        "  return vGS_tan,iD_tan,\n",
        "\n",
        "#parameters\n",
        "clst=['lightskyblue','violet','aqua','cornflowerblue','yellow','w',(.1,.1,.1)] #colors dark bg\n",
        "Nf = 250\n",
        "Vth = 4\n",
        "K=1 #determined by phsysical parameters such as material and device geometry\n",
        "vGS = linspace(0,15,Nf)\n",
        "iD = iD_transfer(vGS,Vth,K)\n",
        "vGS_max = max(vGS)\n",
        "iD_max = max(iD)\n",
        "vGS_t = linspace(Vth, 13,Nf) # series for plotting the tangent line\n",
        "\n",
        "#set up figures\n",
        "fig = plt.figure(figsize=(9,9),facecolor=clst[-1])\n",
        "ax_frame = [[0, 0, .95, .95], [-.1,1.2], [-.1,1.3]]# [pos-boundary, xlim, ylim]\n",
        "xn,xm,yn,ym = ax_frame[1]+ax_frame[2]\n",
        "ax=fig.add_axes(ax_frame[0],xlim=ax_frame[1], ylim=ax_frame[2],fc='none',) #no face color\n",
        "ax.axis('off')\n",
        "ax.annotate(\"\", (xm, 0), (xn, 0),arrowprops={'arrowstyle':\"->\",'color':clst[-2]}) #x-axis\n",
        "ax.annotate(\"\", (0,ym), (0,yn),arrowprops={'arrowstyle':\"->\",'color':clst[-2]}) #y-axis\n",
        "ax.text(xm-.02,0,r'$v_{GS}$',size=24,va='top',ha='right',color=clst[-2]) #x-axis label\n",
        "ax.text(.02,ym,r'$i_{D}$',size=24,va='top',color=clst[-2]) #x-axis label\n",
        "# ax.text(.7,1,'MOSFET Transfer Curve',ha='center',size=36,color=clst[2])\n",
        "ax.set_title('MOSFET Transfer Curve',y=.9,ha='center',size=26,color=clst[2])\n",
        "line_iD = ax.plot([],[],lw=3,color=clst[1])[0]\n",
        "line_tangent = ax.plot([],[],lw=1.5,color=clst[4])[0]\n",
        "line_dot = ax.plot([],[],ls='',marker='o',markersize = 8, color=clst[4])[0]\n",
        "txt_gm = ax.text(0,0,r'$g_m=\\frac{\\partial i_D}{\\partial v_{GS}}$',size=22,\n",
        "                 color=clst[-2],va='top',alpha=0)\n",
        "# animation function. This is called sequentially\n",
        "def animate(i):\n",
        "  ii = i%Nf\n",
        "  k = int(i/Nf)\n",
        "  if k==0:\n",
        "    line_iD.set_data(vGS[:ii+1]/vGS_max,iD[:ii+1]/iD_max)\n",
        "  else:\n",
        "    vGS_tan,iD_tan = gm(vGS_t[ii],Vth,K)\n",
        "    line_tangent.set_data(vGS_tan/vGS_max,iD_tan/iD_max)\n",
        "    line_dot.set_data([vGS_t[ii]/vGS_max],[iD_transfer(vGS_t[ii],Vth,K)/iD_max])\n",
        "    txt_gm.set_position((vGS_t[ii]/vGS_max,iD_transfer(vGS_t[ii],Vth,K)/iD_max))\n",
        "    txt_gm.set_alpha(1.0)\n",
        "  return\n",
        "anim = animation.FuncAnimation(fig, animate, frames=Nf*2, interval=20)\n",
        "# anim #uncomment to generate animation in the output area\n",
        "anim.save(\"MOSFET_transferConductance.mp4\", fps=50, dpi = 120) #uncomment to save"
      ]
    }
  ]
}