Aim-TTi-SMU4201-QPX750SP-MOSFET-Characterization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1585da3-e9f4-4d7f-b1e9-82163c5e5655",
   "metadata": {},
   "source": [
    "# Aim-TTi SMU4201 (and QPX750SP) - MOSFET Characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4febfcf4-4fa1-4d4d-989e-3e44c62c0131",
   "metadata": {},
   "source": [
    "Install and import dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae0c4c9c-c9de-4ba2-bcb7-759b1f31b923",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --quiet pyvisa pyvisa-py Pillow matplotlib pandas\n",
    "\n",
    "import PIL\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pyvisa\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "from IPython.display import clear_output\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11cc657-d88e-40fc-b314-28c6f9c4a434",
   "metadata": {},
   "source": [
    "## Connect to the Aim-TTi SMU4201 & QPX750SP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d0a254-42f6-4115-b88e-ee079ae709cb",
   "metadata": {},
   "source": [
    "Connect to the Aim-TTi SMU4201 via LAN / LXI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08fcf713-abe6-4bff-a72a-bed9f03d5811",
   "metadata": {},
   "outputs": [],
   "source": [
    "rm = pyvisa.ResourceManager()\n",
    "smu_4201 = rm.open_resource(\"TCPIP::192.168.0.132::5025::SOCKET\", write_termination = '\\n',read_termination='\\r\\n')\n",
    "qpx_750sp = rm.open_resource(\"TCPIP::192.168.0.133::5025::SOCKET\", write_termination = '\\n',read_termination='\\r\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988ac8f6-f7e4-4b59-a53b-5c7a96bd8844",
   "metadata": {},
   "source": [
    "Get device info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88740913-e6a1-4b7a-ac6f-ea68a46b68ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMU4201 device info: THURLBY THANDAR,SMU4201,575620,1.4.5-1.1-1.7-1.2-1.0\n",
      "QPX750SP device info: THURLBY THANDAR, QPX750SP, 545155, 1.01-1.01\n"
     ]
    }
   ],
   "source": [
    "print(\"SMU4201 device info: %s\" % smu_4201.query(\"*IDN?\"))\n",
    "print(\"QPX750SP device info: %s\" % qpx_750sp.query(\"*IDN?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e446d6ad-b923-48c2-aa6e-d4f0d9e544d4",
   "metadata": {},
   "source": [
    "## MOSFET Characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad6afdd-a115-4bcb-9d3c-808932f673f3",
   "metadata": {},
   "source": [
    "Instrument setup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d321dd42-4284-4075-ae5e-3c3ed233be19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset to defaults\n",
    "smu_4201.write(\"*CLS; *RST\")\n",
    "time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f60427-b35b-40c8-b54d-81c913606cbe",
   "metadata": {},
   "source": [
    "Charge, discharge and utility functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "740c9133-5a8b-4d2d-8792-dc17d7d0bccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configure Measure Resistence (MR) mode\n",
    "smu_4201.write(\"SYSTem:FUNCtion:MODE %s\" % \"MEASURERESistance\")\n",
    "smu_4201.write(\"SENSe:RESistance:TERMinals %s\" % \"4WIRe\")\n",
    "smu_4201.write(\"SENSe:RESistance:VOLTage:LIMit %s\" % \"100 mV\")\n",
    "smu_4201.write(\"SENSe:RESistance:FIXed:CURRent %s\" % \"100 mA\")\n",
    "\n",
    "qpx_750sp.write(\":VOLT 0.0\")\n",
    "qpx_750sp.write(\":CURR 0.1\")\n",
    "\n",
    "# output on\n",
    "smu_4201.write(\"OUTPut:STATe %s\" % \"ON\")\n",
    "qpx_750sp.write(\"OUTPut:STATe %s\" % \"ON\")\n",
    "\n",
    "time.sleep(10)\n",
    "\n",
    "data_idx = []\n",
    "data_res = []\n",
    "\n",
    "range = np.arange(0.1, 15.001, 0.1)\n",
    "for voltage in range:\n",
    "    volt_str = \"%.2f\" % voltage\n",
    "    qpx_750sp.write(\":VOLTage:LEVel:IMMediate %s\" % volt_str) # set output voltage \n",
    "\n",
    "    time.sleep(3) # wait to stabilize\n",
    "\n",
    "    meas_resistence = float(smu_4201.query(\"MEASure:PRIMary:LIVEdata?\"))\n",
    "   \n",
    "    data_idx.append(voltage)\n",
    "    data_res.append(meas_resistence)\n",
    "\n",
    "# output off\n",
    "smu_4201.write(\"OUTPut:STATe %s\" % \"OFF\")\n",
    "qpx_750sp.write(\"OUTPut:STATe %s\" % \"OFF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed022322-85a4-4277-bc4d-8e6b60cb0172",
   "metadata": {},
   "source": [
    "## Gate Voltage vs. Resistance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f96654-715d-4fa5-98e5-16b716e34384",
   "metadata": {},
   "source": [
    "Charge curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "371a792f-3f70-402c-91fb-cf62de4c2695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe8334333d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color1 = plt.cm.viridis(0)\n",
    "color2 = plt.cm.viridis(0.5)\n",
    "color3 = plt.cm.viridis(.9)\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12, 7))\n",
    "ax1.set_yscale('log')\n",
    "#ax1.set_xscale('log')\n",
    "\n",
    "ax1.set_xlabel('Gate Voltage (V)', color=color1)\n",
    "ax1.set_ylabel('Resistance (Ω)', color=color2)\n",
    "\n",
    "p1, = ax1.plot(data_idx, data_res, marker='', color = color2, label=\"Resistance (Ω)\")\n",
    "\n",
    "ax1.legend(handles=[p1], loc='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf5fd8b-b2be-4811-b597-4ab5da30fe75",
   "metadata": {},
   "source": [
    "Close connection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "898f4340-94c9-442d-bc1e-d63369d0b75d",
   "metadata": {},
   "outputs": [],
   "source": [
    "smu_4201.close()\n",
    "qpx_750sp.close()"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}