6.Coloumb_Daimond_3gates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ASRL1::INSTR',\n",
       " 'ASRL2::INSTR',\n",
       " 'ASRL10::INSTR',\n",
       " 'GPIB0::5::INSTR',\n",
       " 'GPIB0::18::INSTR')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyvisa as visa\n",
    "import matplotlib.pyplot as plt\n",
    "import os, time, threading, datetime, pymeasure,tqdm\n",
    "from tqdm import trange,tqdm\n",
    "from time import sleep\n",
    "from datetime import date\n",
    "from IPython import display\n",
    "from scipy.optimize import curve_fit\n",
    "from pymeasure.adapters import VISAAdapter\n",
    "from pathlib import Path\n",
    "from pymeasure.instruments.keithley import Keithley2400, Keithley2000\n",
    "import qcodes.instrument_drivers.oxford.MercuryiPS_VISA as mercury_ips\n",
    "current_work_directory = os.getcwd()\n",
    "os.chdir('C:/Users/USER/Device_Library/Device_Library/')\n",
    "from my_SR830 import my_SR830\n",
    "from my_K213 import my_K213\n",
    "from my_K236 import my_K236\n",
    "import device_query\n",
    "os.chdir(current_work_directory)\n",
    "now = datetime.datetime.now()\n",
    "# print(now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "visa.ResourceManager().list_resources()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GOTO_THREAD(K213, step, delay_time, port_id, target, time_shift):\n",
    "    print(\"Port ID = %d , target = %.4f .\"%(port_id, target))\n",
    "    time.sleep(time_shift)\n",
    "#     K213.GOTO(target, step, delay_time, reset_range=True,auto_range=True, reset_port=True, port=port_id)  old syntax!!\n",
    "    K213.GOTO(target, step, delay_time, port = port_id, reset_range=True,auto_range=True)\n",
    "    K213.GET_OUTPUT(port_id)\n",
    "    time.sleep(1)\n",
    "    \n",
    "def TO_ZERO_THREAD(K213, step, delay_time, port_id, time_shift):\n",
    "    print(\"Port ID = %d , target = %.4f .\"%(port_id, 0.0))\n",
    "    time.sleep(time_shift)\n",
    "    K213.ZERO(step, delay_time, port_id)\n",
    "    K213.GET_OUTPUT(port_id)\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Port 1 is selected.\n",
      "2020-09-30\n"
     ]
    }
   ],
   "source": [
    "SR830_GPIB_Addr = 5\n",
    "K213_GPIB_Addr = 18\n",
    "srs_source = my_SR830.MY_SR830(SR830_GPIB_Addr)\n",
    "K213 = my_K213.MY_KEITHLEY213(K213_GPIB_Addr, port=1)\n",
    "port_list = [1,2,3]  #輸入要固定的port\n",
    "# comp = 1e-7 #輸入236的compliance                       ####記得檢查GPIB號碼 CHECK GPIB####\n",
    "# k236_source_1 = my_K236.MY_KEITHLEY236(23, 'SVMI',comp) ### (GPIB#, 給(Source)V量(Measure)I , Compliance)\n",
    "# print(k236_source_1.compliance)    \n",
    "print(date.today())\n",
    "\n",
    "#[2,3,4] 如果多P4需要當GATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bias(DC):  ####  REMEMBER　TO INPUT THE SELECTED DEPLETION GATE VOLTAGE  ####\n",
    "    min_V = 4.3    #initial voltage\n",
    "    max_V = 5.5    #final voltage\n",
    "    data_points = 480  #resolution per sweep\n",
    "\n",
    "    gate_voltage_apply_1 = np.append(np.linspace(min_V, max_V,data_points),np.linspace(max_V, min_V,data_points)) # for 213 port 1\n",
    "    gate_voltage_apply_2 = 4.72\n",
    "    gate_voltage_apply_3 = 4.49 # for 213 port 2 & 3\n",
    "    DC_voltage = DC\n",
    "\n",
    "    measure_wait_time=3  #電壓到時等一段時間穩定後再量測\n",
    "    step = 0.01         #調變電壓每一步的大小\n",
    "    delay_time = 0.1    #每次調變所等待的時間\n",
    "    N_ave = 10 #在一個電壓職重複量測幾次\n",
    "    ave_wait = 2 #根據 830 的 TIME CONSTANT 調整\n",
    "\n",
    "    #############################################################\n",
    "    SD_voltage_apply = 1\n",
    "\n",
    "    srs_goto_step = 0.05\n",
    "    srs_goto_delay_time=0.5\n",
    "    srs_source.input_config = 'A'\n",
    "    srs_source.input_grounding = 'Float'\n",
    "    srs_source.frequency = 87.11 # change the lock-in frequency\n",
    "    srs_source.time_constant = 0.3 # change the lock-in time constant\n",
    "    srs_source.sensitivity = 5e-5 # change the lock-in sensitivity\n",
    "    srs_source.channel1 = 'X'\n",
    "    srs_source.channel2 = 'Y'\n",
    "\n",
    "    # srs_source.GOTO(SD_voltage_apply, srs_goto_step, srs_goto_delay_time)   #lockin test ramp\n",
    "    # Lock-in 放大率(到K2000需要乘上倍數)\n",
    "    # senstivity 2mv = 5000倍\n",
    "    # 10V / senstivity\n",
    "    #############################################################\n",
    "    repeat = 0\n",
    "    folder_path = \"C:/Users/USER/Sean/2020-09-29 QD 4.72_4.49 diamond fine_step negative/\" ##儲存的地方\n",
    "    file_name = 'DC_%smV_repeat%s_at_%s'\\\n",
    "    %(DC_voltage,repeat,date.today())##儲存的檔名  ##看分壓多少\n",
    "    print('檔名會叫做 >> %s'%file_name)\n",
    "\n",
    "    overwrite = 'Y'\n",
    "    if os.path.isfile('%s%s.txt'%(folder_path,file_name))==True:\n",
    "        overwrite = input('Do you want to overwtite the existing file? >>> Y/N  ')\n",
    "    if overwrite == 'Y':\n",
    "        file_name = file_name\n",
    "    else:\n",
    "        file_name = '##'\n",
    "        print(\"please create a new file\")\n",
    "        raise RuntimeError('!!!!  File exsit, please name a new file  !!!!')\n",
    "    #############################################################\n",
    "    #畫圖\n",
    "    fontsize = 6\n",
    "    drawing_resolution=10\n",
    "\n",
    "    #分壓比例(Source Drain SR830)\n",
    "    divider_ratio= 1e-5\n",
    "    #放大器比例(Source Drain SR830)\n",
    "    Amplifier_gain= 1e6    \n",
    "\n",
    "    ##歸零#############################################################\n",
    "    threads = []\n",
    "    shift = 4e-2 #同時給兩個port會當機所以要有一個小時間差\n",
    "\n",
    "    print(\"Start...\")\n",
    "    for i in range(len(port_list)):\n",
    "        time_shift = shift*i\n",
    "        threads.append(threading.Thread(target = TO_ZERO_THREAD, args = (K213, step, delay_time,port_list[i], time_shift)))\n",
    "        threads[i].start()\n",
    "    for i in range(len(port_list)):\n",
    "        threads[i].join()\n",
    "#     K213.GOTO(0.0, step, delay_time, port=4,reset_range=True, auto_range = True, range_m=1)\n",
    "    print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "    print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "    print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "    print('DC is now at',K213.GET_OUTPUT(port=4),'Volts')\n",
    "#     k236_source_1.GOTO(0.0, step, delay_time, comp, range_m = 0, reset_compliance=False)\n",
    "#     srs_source.GOTO(0.004, srs_goto_step, srs_goto_delay_time) \n",
    "#     print(\"Done!\")\n",
    "    ##給固定電壓############################################################\n",
    "    #Source Drain\n",
    "    srs_source.GOTO(SD_voltage_apply, srs_goto_step, srs_goto_delay_time)       ##### SR-830設定(固定SD)\n",
    "    #k236_source_1.GOTO(DC, step, delay_time, comp, range_m = 0, reset_compliance=False) 用236給GATE\n",
    "    print('Applying DC ...')\n",
    "    K213.GOTO(DC, step, delay_time, port=4,reset_range=True, auto_range = True, range_m=1)\n",
    "    print('Done!')\n",
    "    print('DC is now at',K213.GET_OUTPUT(port=4),'Volts')\n",
    "    #Gates\n",
    "    threads = []\n",
    "    target = [min_V,gate_voltage_apply_2,gate_voltage_apply_3] #分別目標值\n",
    "    shift = 0.1 #port 和 port之間開始便電壓的時間間隔\n",
    "\n",
    "    print(\"Start applying gate voltages...\")\n",
    "    for i in range(len(port_list)):\n",
    "        time_shift = shift*i\n",
    "        threads.append(threading.Thread(target = GOTO_THREAD, args = (K213, step, delay_time,port_list[i],target[i],time_shift)))\n",
    "        threads[i].start()\n",
    "    for i in range(len(port_list)):\n",
    "        threads[i].join()\n",
    "    print(\"Done!\")\n",
    "    print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "    print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "    print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "    \n",
    "    ##開始量測##################################################################    \n",
    "    K213.GOTO(min_V, step, delay_time, port=1,\\\n",
    "              reset_range=True, auto_range = True, range_m=1)\n",
    "    print(\"Sweeping gate goes to starting value...\")\n",
    "    print(\"Initialize K213...\")\n",
    "    #len(gate_voltage_apply_1)\n",
    "    current_measure_xx = np.zeros((len(gate_voltage_apply_1), N_ave+1)) #製造0矩陣以放入量測數據\n",
    "    conductance_measure_xx = np.zeros((len(gate_voltage_apply_1), N_ave+1))\n",
    "    plt.figure(figsize=(3,3), dpi=200)\n",
    "    plt.xlim([min_V,max_V])\n",
    "        #plt.xlim([0,max_I])\n",
    "    start = time.time()\n",
    "    print(\"Apply Gate voltage...\")\n",
    "    \n",
    "    for index_V, voltage in enumerate(tqdm(gate_voltage_apply_1)): #####開始掃GATE(P1)並量測記錄\n",
    "            K213.GOTO(voltage, step, delay_time, port=1,\\\n",
    "                reset_range=True, auto_range = True, range_m=1)\n",
    "            time.sleep(measure_wait_time)\n",
    "            Sum_of_current, Sum_of_conductance = 0, 0\n",
    "\n",
    "        ####量測####\n",
    "        \n",
    "            for n in range(N_ave):\n",
    "                current_measure_xx[index_V,n] = srs_source.x/Amplifier_gain   ##讀取Lock_in的值  絕對值:srs_source.magnitude\n",
    "                conductance_measure_xx[index_V,n] = srs_source.x/Amplifier_gain/(SD_voltage_apply*divider_ratio)#dI/dV 只看時便的部分\n",
    "                Sum_of_current += current_measure_xx[index_V,n]\n",
    "                Sum_of_conductance += conductance_measure_xx[index_V,n]\n",
    "                time.sleep(ave_wait)## Delay between measurements\n",
    "            current_measure_xx[index_V,N_ave] = Sum_of_current/float(N_ave) #加上平均數值\n",
    "            conductance_measure_xx[index_V,N_ave] = Sum_of_conductance/float(N_ave) #加上平均數值\n",
    "\n",
    "        ####畫圖####\n",
    "            if (index_V%drawing_resolution==0):\n",
    "                display.clear_output(wait=True)\n",
    "                plt.subplot(4,1, 1)\n",
    "                plt.plot(gate_voltage_apply_1[:index_V+1] , current_measure_xx[:index_V+1,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #           plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "                plt.xlabel(\"$V$ (V)\")\n",
    "                plt.xticks(fontsize=fontsize)\n",
    "                plt.ylabel(\"$I$ (A)\")\n",
    "                plt.yticks(fontsize=fontsize)\n",
    "\n",
    "                plt.subplot(4,1, 2)\n",
    "                plt.plot(gate_voltage_apply_1[:index_V+1] , conductance_measure_xx[:index_V+1,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #           plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "                plt.xlabel(\"$V$ (V)\")\n",
    "                plt.xticks(fontsize=fontsize)\n",
    "                plt.ylabel(\"$G$ (S)\")\n",
    "                plt.yticks(fontsize=fontsize)\n",
    "                display.display(plt.gcf())\n",
    "    #             print(\"Current temperature is %.4f K.\"%triton.GET_CURRENT_TEMPERATURE(control_channel))\n",
    "    time.sleep(0.01)\n",
    "    print(\"Measurement ends.\")\n",
    "    display.clear_output(wait=True)\n",
    "\n",
    "    plt.subplot(4,1, 1)\n",
    "    plt.plot(gate_voltage_apply_1 , current_measure_xx[:,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "    plt.xlabel(\"$V$ (V)\")\n",
    "    plt.xticks(fontsize=fontsize)\n",
    "    plt.ylabel(\"$I$ (A)\")\n",
    "    plt.yticks(fontsize=fontsize)\n",
    "\n",
    "    plt.subplot(4,1, 2)\n",
    "    plt.plot(gate_voltage_apply_1 , conductance_measure_xx[:,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "    plt.xlabel(\"$V$ (V)\")\n",
    "    plt.xticks(fontsize=fontsize)\n",
    "    plt.ylabel(\"$G$ (S)\")\n",
    "    plt.yticks(fontsize=fontsize)\n",
    "    display.display(plt.gcf())\n",
    "    \n",
    "    stop = time.time()\n",
    "    T=(stop-start)/60\n",
    "    print('這次量測花費了 %f 分鐘'%T)\n",
    "    print(\"Source drain goes to zero...\")\n",
    "\n",
    "    # plt.savefig('%s/Rxx.png'%folder_path[index_current],bbox_inches='tight', dpi=200, pad_inches=0.05) ##存圖片\n",
    "    ##存檔############################################################\n",
    "    print(\"saving ... \")\n",
    "    raw_data = np.hstack((gate_voltage_apply_1.reshape(-1,1), current_measure_xx, conductance_measure_xx))\n",
    "    data = np.hstack((gate_voltage_apply_1.reshape(-1,1), current_measure_xx[:,[-1]], conductance_measure_xx[:,[-1]]))\n",
    "    np.savetxt(\"%sraw_data%s.txt\" %(folder_path,file_name),raw_data, fmt=\"%.8e\", \\\n",
    "               header='V(V)\\tI(A)*%d\\tI_avg\\tG(S)*%d\\tG_avg(S)'%(N_ave,N_ave))\n",
    "    np.savetxt('%s%s.txt'%(folder_path,file_name),data, fmt=\"%.8e\",comments='',\\\n",
    "               header='VAG\\tId_avg_of_%s\\tG_avg_of_%s\\\n",
    "               \\n(V)\\t(A)\\t(S)\\\n",
    "               \\nfrom%sVto%sV\\tresolution_%s(V)\\tDC=%smV\\tDG1_%sV_DG2_%sV\\tmeasure_wait_time_%s\\tgoto_step_%s\\tdelay_time_goto_%s'\\\n",
    "               %(N_ave,N_ave,\\\n",
    "                 min_V,max_V,(max_V-min_V)/data_points,DC_voltage,gate_voltage_apply_2,gate_voltage_apply_3,measure_wait_time,step,delay_time))\n",
    "    ##歸零############################################################\n",
    "\n",
    "    threads = []\n",
    "    shift = 4e-2 #同時給兩個port會當機所以要有一個小時間差\n",
    "    print(\"Start...\")\n",
    "    for i in range(len(port_list)):\n",
    "        time_shift = shift*i\n",
    "        threads.append(threading.Thread(target = TO_ZERO_THREAD, args = (K213, step, delay_time,port_list[i], time_shift)))\n",
    "        threads[i].start()\n",
    "    for i in range(len(port_list)):\n",
    "        threads[i].join()\n",
    "    print(\"Done!\")\n",
    "    print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "    print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "    print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "    print('DC was at',K213.GET_OUTPUT(port=4),'Volts')\n",
    "    #k236_source_1.GOTO(0.0, step, delay_time, comp, range_m = 0, reset_compliance=False)\n",
    "    #srs_source.GOTO(0.004, srs_goto_step, srs_goto_delay_time) \n",
    "    #\"%s/I_V_G_V%s.txt\" %(folder_path,file_name)\n",
    "    # print (now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voltage divider (DC) at 1e-3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:159: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:167: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "這次量測花費了 375.347497 分鐘\n",
      "Source drain goes to zero...\n",
      "saving ... \n",
      "Start...\n",
      "Port ID = 1 , target = 0.0000 .Port ID = 2 , target = 0.0000 .\n",
      "\n",
      "Port ID = 3 , target = 0.0000 .\n",
      "Done!\n",
      "P1 is now at 0.0 Volts\n",
      "P2 is now at 0.0 Volts\n",
      "P3 is now at 0.0 Volts\n",
      "DC was at 0.0 Volts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bias(0)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}