Aim-TTi SMU4201 Road Test - Zener Diode Characterization

Aim-TTi-SMU4201-Zener-Diode-Characterization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1585da3-e9f4-4d7f-b1e9-82163c5e5655",
   "metadata": {},
   "source": [
    "# Aim-TTi SMU4201 - Zener Diode Characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4febfcf4-4fa1-4d4d-989e-3e44c62c0131",
   "metadata": {},
   "source": [
    "Install and import dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ae0c4c9c-c9de-4ba2-bcb7-759b1f31b923",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --quiet pyvisa pyvisa-py Pillow matplotlib\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11cc657-d88e-40fc-b314-28c6f9c4a434",
   "metadata": {},
   "source": [
    "## Connect to the Aim-TTi SMU4201:"
   ]
  },
  {
   "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": 133,
   "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988ac8f6-f7e4-4b59-a53b-5c7a96bd8844",
   "metadata": {},
   "source": [
    "Get device info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "88740913-e6a1-4b7a-ac6f-ea68a46b68ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'THURLBY THANDAR,SMU4201,575620,1.4.5-1.1-1.7-1.2-1.0'"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smu_4201.query(\"*IDN?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e446d6ad-b923-48c2-aa6e-d4f0d9e544d4",
   "metadata": {},
   "source": [
    "## Diode Characterization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad6afdd-a115-4bcb-9d3c-808932f673f3",
   "metadata": {},
   "source": [
    "Instrument setup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d321dd42-4284-4075-ae5e-3c3ed233be19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters\n",
    "current_limit = 0.01\n",
    "\n",
    "# Reset to defaults\n",
    "smu_4201.write(\"*CLS; *RST\")\n",
    "time.sleep(1)\n",
    "\n",
    "# Configure Source Voltage (SV) mode\n",
    "smu_4201.write(\"SYSTem:FUNCtion:MODE %s\" % \"SOURCEVOLTage\")\n",
    "smu_4201.write(\"SOURce:VOLTage:FIXed:LEVel %s\" % \"0.000 V\")\n",
    "smu_4201.write(\"SOURce:VOLTage:CURRent:LIMit %s\" % (\"%0.6f A\" % current_limit))\n",
    "smu_4201.write(\"SOURce:VOLTage:TERMinals %s\" % \"4WIRe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed022322-85a4-4277-bc4d-8e6b60cb0172",
   "metadata": {},
   "source": [
    "Helper functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "e5db4947-224b-4891-b3c7-6e75fe7fbf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def limit_reached(voltage, voltage_start, voltage_end):\n",
    "    if voltage_start < voltage_end:\n",
    "        return voltage < voltage_start or voltage > voltage_end\n",
    "    else:\n",
    "        return voltage < voltage_end or voltage > voltage_start\n",
    "\n",
    "def higher_resulution_needed(measurement, last_measurement, target_change):\n",
    "    return abs(last_measurement - measurement) > target_change\n",
    "\n",
    "def adaptive_sweep(\n",
    "        voltage_start,\n",
    "        voltage_end,\n",
    "        voltage_step = 0.25,\n",
    "        voltage_step_max = 0.25,\n",
    "        voltage_step_min = 0.005,\n",
    "        current_limit = current_limit,\n",
    "        meas_target_change = current_limit / 100.0,\n",
    "        settle_time = 0.1):\n",
    "\n",
    "    # data storage\n",
    "    data_idx = []\n",
    "    data_volt_set = []\n",
    "    data_volt_meas = []\n",
    "    data_curr_meas = []\n",
    "\n",
    "    voltage = voltage_start\n",
    "    last_current = 0.00\n",
    "    \n",
    "    while True:\n",
    "        # check voltage limit\n",
    "        if limit_reached(voltage, voltage_start, voltage_end):\n",
    "            print(\"Voltage limit reached %0.10f\" % voltage);\n",
    "            break\n",
    "\n",
    "        # set voltage\n",
    "        volt_str = \"%.10f V\" % voltage\n",
    "        clear_output(wait=True)\n",
    "        print(\"Voltage %s\" % voltage)\n",
    "        \n",
    "        smu_4201.write(\"SOURce:VOLTage:FIXed:LEVel %s\" % volt_str)\n",
    "\n",
    "        # wait a bit, so the measurment settles\n",
    "        time.sleep(settle_time)\n",
    "\n",
    "        # retrive the voltage and current measurements\n",
    "        volt_meas = float(smu_4201.query(\"MEASure:PRIMary:LIVEdata?\"))\n",
    "        curr_meas = float(smu_4201.query(\"MEASure:SECondary:LIVEdata?\"))\n",
    "\n",
    "        # adaptive resolution        \n",
    "        if higher_resulution_needed(curr_meas, last_current, meas_target_change):\n",
    "            print(\"change needed %s old=%0.10f new=%0.10f target=%0.10f\" % (volt_str, last_current, curr_meas, meas_target_change))\n",
    "            # double the resolution\n",
    "            if abs(voltage_step) / 2.0 >= voltage_step_min:\n",
    "                voltage -= voltage_step\n",
    "                voltage_step /= 2.0\n",
    "                voltage += voltage_step\n",
    "                continue\n",
    "\n",
    "        # save last current measurement\n",
    "        last_current = curr_meas;\n",
    "\n",
    "        # set voltage to 0.0 and wait a bit\n",
    "        smu_4201.write(\"SOURce:VOLTage:FIXed:LEVel %s\" % \"0.0 V\")\n",
    "        time.sleep(settle_time)\n",
    "        \n",
    "        # check current limit\n",
    "        if limit_reached(abs(curr_meas), 0, current_limit * 0.99):\n",
    "            print(\"current limit reached %10f\" % curr_meas)\n",
    "            break\n",
    "\n",
    "        # save the measurements\n",
    "        data_idx.append(voltage)\n",
    "        data_volt_set.append(voltage)\n",
    "        data_volt_meas.append(volt_meas)\n",
    "        data_curr_meas.append(curr_meas)\n",
    "\n",
    "        # advance the voltage\n",
    "        voltage += voltage_step;\n",
    "\n",
    "    return data_idx, data_volt_set, data_volt_meas, data_curr_meas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57429ac-ce24-4a40-97e9-9392aee2824c",
   "metadata": {},
   "source": [
    "Peform adaptive sweep with positive and negative voltages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "39ecbaa8-627b-4777-a140-d0a69e6e7f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# turn on the output\n",
    "smu_4201.write(\"OUTPut:STATe %s\" % \"ON\")\n",
    "time.sleep(2)\n",
    "\n",
    "# Positive voltages\n",
    "pos_idx, pos_volt_set, pos_volt_meas, pos_curr_meas \\\n",
    "    = adaptive_sweep(0.0, 1.0)\n",
    "\n",
    "# Negative voltages\n",
    "neg_idx, neg_volt_set, neg_volt_meas, neg_curr_meas \\\n",
    "    = adaptive_sweep(0.0, -7.0, -0.25)\n",
    "\n",
    "# turn off the output\n",
    "smu_4201.write(\"OUTPut:STATe %s\" % \"OFF\")\n",
    "\n",
    "# results\n",
    "data_idx = neg_idx + pos_idx\n",
    "data_volt_set = neg_volt_set + pos_volt_set\n",
    "data_volt_meas = neg_volt_meas + pos_volt_meas\n",
    "data_curr_meas = neg_curr_meas + pos_curr_meas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03444afe-2d81-4aee-87da-c9cf35cb0b8b",
   "metadata": {},
   "source": [
    "Display the measurment data as an I/V Curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "371a792f-3f70-402c-91fb-cf62de4c2695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1bf0978110>"
      ]
     },
     "execution_count": 211,
     "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": [
    "data_idx, data_curr_meas, data_volt_meas = zip(*sorted(zip(data_idx, data_curr_meas, data_volt_meas)))\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(12, 7))\n",
    "color1 = plt.cm.viridis(0)\n",
    "color2 = plt.cm.viridis(0.5)\n",
    "color3 = plt.cm.viridis(.9)\n",
    "\n",
    "ax1.set_xlabel('Voltage (set)', color=color1)\n",
    "ax1.set_ylabel('Current (measured)', color=color2)\n",
    "\n",
    "p1, = ax1.plot(data_idx, data_curr_meas, marker='', color = color2, label=\"Current (measured)\")\n",
    "\n",
    "ax1.legend(handles=[p1], loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aee63ae-1684-471d-bd69-d9bf87a9ba42",
   "metadata": {},
   "source": [
    "Test voltages of adaptive sweeping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "9f6254c3-1c9f-4a08-9ea8-02c9b0c48d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f1bf079e850>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(12, 7))\n",
    "ax2 = ax1.twinx()\n",
    "color1 = plt.cm.viridis(0)\n",
    "ax1.set_xlabel('Test Voltage (set)', color=color1)\n",
    "p1, = ax1.plot(data_idx, data_idx, marker='o', color = color1, label=\"Test Voltage (set)\")\n",
    "p2, = ax2.plot(data_idx, data_curr_meas, marker='o', color = color2, label=\"Current (measured)\")\n",
    "ax1.legend(handles=[p1,p2], loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35e8759-6558-4984-8cee-e136723cb696",
   "metadata": {},
   "source": [
    "Close the remote connection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "eea5b454-9e20-4b15-a256-db012bf6c95a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smu_4201.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
}