{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# QCoDeS Example with with Keithley 2600 series" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import qcodes as qc\n", "from qcodes.dataset import (\n", " LinSweep,\n", " do0d,\n", " do1d,\n", " initialise_database,\n", " new_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.instrument_drivers.Keithley import Keithley2614B" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Connected to: Keithley Instruments Inc. 2614B (serial:4305420, firmware:3.2.2) in 0.77s\n" ] }, { "data": { "text/plain": [ "'keithley'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a station to hold all the instruments\n", "\n", "station = qc.Station()\n", "\n", "# instantiate the Keithley and add it to the station\n", "\n", "keith = Keithley2614B(\"keithley\", \"USB0::0x05E6::0x2614::4305420::INSTR\")\n", "station.add_component(keith)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Keithley 2600 has two channels, here called `smua` and `smub` in agreement with the instrument manual.\n", "\n", "The two channels are basically two separate instruments with different integration times (`nplc`), operation modes (`mode`) etc." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "keithley:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "IDN :\t{'vendor': 'Keithley Instruments Inc.', 'model': '2614B', '...\n", "display_settext :\tNone \n", "timeout :\t5 (s)\n", "keithley_smua:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "curr :\t8.7023e-07 (A)\n", "fastsweep :\tNot available \n", "limiti :\t0.1 (A)\n", "limitv :\t20 (V)\n", "linefreq :\t50 (Hz)\n", "measure_autorange_i_enabled :\tFalse \n", "measure_autorange_v_enabled :\tTrue \n", "measurerange_i :\t0.1 (A)\n", "measurerange_v :\t200 (V)\n", "mode :\tcurrent \n", "nplc :\t0.05 \n", "output :\tTrue \n", "res :\t2.2985e+07 (Ohm)\n", "source_autorange_i_enabled :\tTrue \n", "source_autorange_v_enabled :\tFalse \n", "sourcerange_i :\t0.1 (A)\n", "sourcerange_v :\t20 (V)\n", "time_axis :\tNot available (s)\n", "timetrace :\tNot available (A)\n", "timetrace_dt :\t0.001 (s)\n", "timetrace_mode :\tcurrent \n", "timetrace_npts :\t500 \n", "volt :\t20.002 (V)\n", "keithley_smub:\n", "\tparameter value\n", "--------------------------------------------------------------------------------\n", "curr :\t-1.8239e-12 (A)\n", "fastsweep :\tNot available \n", "limiti :\t0.1 (A)\n", "limitv :\t20 (V)\n", "linefreq :\t50 (Hz)\n", "measure_autorange_i_enabled :\tTrue \n", "measure_autorange_v_enabled :\tTrue \n", "measurerange_i :\t1e-07 (A)\n", "measurerange_v :\t0.2 (V)\n", "mode :\tvoltage \n", "nplc :\t1 \n", "output :\tFalse \n", "res :\t3.5934e+07 (Ohm)\n", "source_autorange_i_enabled :\tTrue \n", "source_autorange_v_enabled :\tTrue \n", "sourcerange_i :\t1e-07 (A)\n", "sourcerange_v :\t0.2 (V)\n", "time_axis :\tNot available (s)\n", "timetrace :\tNot available (A)\n", "timetrace_dt :\t0.001 (s)\n", "timetrace_mode :\tcurrent \n", "timetrace_npts :\t500 \n", "volt :\t-6.3276e-05 (V)\n" ] } ], "source": [ "# Get an overview of the settings\n", "#\n", "# You will notice that the two channels have identical parameters but\n", "# potentially different values for them\n", "#\n", "keith.print_readable_snapshot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic operation\n", "\n", "Each channel operates in either `voltage` or `current` mode. The mode controls the _source_ behaviour of the instrument, i.e. `voltage` mode corresponds to an amp-meter (voltage source, current meter) and vice versa." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measured one current value: -3.40939e-06 A\n" ] } ], "source": [ "# Let's set up a single-shot current measurement\n", "# on channel a\n", "\n", "keith.smua.mode(\"voltage\")\n", "keith.smua.nplc(\n", " 0.05\n", ") # 0.05 Power Line Cycles per measurement. At 50 Hz, this corresponds to 1 ms\n", "keith.smua.sourcerange_v(20)\n", "keith.smua.measurerange_i(0.1)\n", "\n", "keith.smua.volt(1) # set the source to output 1 V\n", "keith.smua.output(\"on\") # turn output on\n", "curr = keith.smua.curr()\n", "keith.smua.output(\"off\")\n", "\n", "print(f\"Measured one current value: {curr} A\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Four-probe measurement \n", "\n", "Enabling four-probe measurements is simple." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Select channel to work with\n", "smub = keith.smub\n", "\n", "# Set up for four-probe measurement\n", "smub.mode(\"current\") # Source current, measure voltage\n", "smub.nplc(1.0)\n", "smub.limitv(20)\n", "smub.source_autorange_i_enabled(True)\n", "smub.measure_autorange_v_enabled(True)\n", "\n", "# Set sense mode\n", "smub.four_wire_measurement(True)\n", "\n", "# Make sure output is enabled\n", "smub.output(True)\n", "\n", "# Sweep parameters\n", "i_start = 0 # A\n", "i_stop = 0.1e-6 # A (0.1 µA)\n", "n_points = 101\n", "settle_delay = 0.001\n", "\n", "# Take measurements\n", "data, _, _ = do1d(smub.curr, i_start, i_stop, n_points, settle_delay, smub.volt)\n", "\n", "# Set params to safe values\n", "smub.curr(0)\n", "smub.output(False)\n", "\n", "plot_dataset(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fast IV or VI curves\n", "\n", "Onboard the Keithley 2600 sits a small computer that can interpret `Lua` scripts. This can be used to make fast IV- or VI-curves and is supported by the QCoDeS driver. To make IV- or VI-curves the driver has the ParameterWithSetpoints fastsweep, which has 3 modes: 'IV', 'VI' and 'VIfourprobe'. The Modes 'IV' and 'VI'are two probe measurements, while the mode 'VIfourprobe' makes a four probe measurement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1D Fast Sweep\n", "\n", "Let's make a fast IV curve by sweeping the voltage from 1 V to 2 V in 500 steps.\n", "Create a `LinSweep` object and pass it to `setup_fastsweep`, then call `do0d` on `fastsweep`. \n", "\n", "*NOTE on LinSweep:* `LinSweep` isn't specifically required here. We just need a `_LinSweepLike` object to pass, which requires `param`, `delay`, and `num_points` attributes and a `get_setpoints()` method.\n", "\n", "*NOTE on fastsweep:* Users may decide which channel the current/voltage is measured from during a fastsweep. It is usually expected to measure the channel that is being swept (which is the default behavior), but in cases where your measurement requires you to collect data from some other terminal (e.g., if you are sweeping voltage on terminal A and want to measure current on terminal B), you can set `measure_inner_channel=False` in `setup_fastsweep`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Configure a 1D fastsweep using LinSweep\n", "fast_sweep_1d = LinSweep(keith.smua.volt, 1, 2, 500)\n", "keith.smua.setup_fastsweep(fast_sweep_1d, mode=\"IV\", measure_inner_channel=True)\n", "\n", "# Perform the measurement\n", "ds, _, _ = do0d(keith.smua.fastsweep)\n", "plot_dataset(ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2D Fast Sweep\n", "\n", "For 2D sweeps, provide both inner and outer `LinSweep` objects. The inner sweep runs to completion for each step of the outer sweep. The measurement is performed on the **inner** channel (the one from the first LinSweep).\n", "\n", "Here we sweep `smua.volt` (inner/fast axis) while stepping `smub.volt` (outer/slow axis):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Configure a 2D fastsweep: inner on smua, outer on smub\n", "inner_sweep = LinSweep(keith.smua.volt, -1, 1, 100) # inner (fast axis)\n", "outer_sweep = LinSweep(keith.smub.volt, 0, 0.5, 20) # outer (slow axis)\n", "\n", "keith.smua.setup_fastsweep(inner_sweep, outer_sweep, mode=\"IV\")\n", "\n", "# Perform the measurement - call fastsweep on the inner channel (smua)\n", "ds, _, _ = do0d(keith.smua.fastsweep)\n", "plot_dataset(ds)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Time Trace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can measure current or voltage as a function of time, as well. Let us consider the case in which we would like to have the time trace of current. We, then, first set our trace mode accordingly" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "keith.smua.timetrace_mode(\"current\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The latter should not be confused with the channel mode. Next, we register our parameter" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "timemeas = qc.Measurement()\n", "timemeas.register_parameter(keith.smua.timetrace)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The total time interval is returned by `time_axis` method in accordance with the integration time `dt` and the total number of points `npts` with default values of 1 ms and 500, respectively. Specifically, the time trace will be obtained for an interval of `[0, dt*npts]`. Thus, for the default values, we shall have a time trace of current for `500 ms` calculated at 500 steps." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "keith.smua.timetrace_dt()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "500" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "keith.smua.timetrace_npts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 157. \n" ] }, { "data": { "text/plain": [ "([],\n", " [None])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "initialise_database()\n", "new_experiment(name=\"tutorial_exp\", sample_name=\"no sample\")\n", "\n", "with timemeas.run() as datasaver:\n", " somenumbers = keith.smua.timetrace.get()\n", " datasaver.add_result(\n", " (keith.smua.timetrace, somenumbers),\n", " (keith.smua.time_axis, keith.smua.time_axis.get()),\n", " )\n", "\n", "data = datasaver.dataset\n", "plot_dataset(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us get another trace for 2ms integration time with 600 steps" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "keith.smua.timetrace_dt(0.002)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "keith.smua.timetrace_npts(600)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 158. \n" ] }, { "data": { "text/plain": [ "([],\n", " [None])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "with timemeas.run() as datasaver:\n", " somenumbers = keith.smua.timetrace.get()\n", " datasaver.add_result(\n", " (keith.smua.timetrace, somenumbers),\n", " (keith.smua.time_axis, keith.smua.time_axis.get()),\n", " )\n", "\n", "data = datasaver.dataset\n", "plot_dataset(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, we can get the time trace for voltage via changing the mode" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "keith.smua.timetrace_mode(\"voltage\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, one should re-register the time trace parameter to have the correct units and labels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recalibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it happens that Keithley SMU measures a different value from what was assigned as the setpoint; e.g. when setting the voltage to ``-2 mV``, the SMU would measure an output of ``-1.85 mV``. This likely calls for recalibration of the Keithley SMU against e.g. a calibrated DMM. After this recalibration the measured value matches the setpoint.\n", "\n", "Below is a description of using a calibration routine that is based off the information from the instrument manual about performing the calibration.\n", "\n", "To run this routine, connect the Keithley SMU HI voltage output to the DMM voltage HI input, and the SMU LO output to the DMM voltage LO input (note that this is a different setup as indicated in the Keithley manual)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %% Imports\n", "\n", "# The calibration functions and utils\n", "from qcodes.calibrations.keithley import (\n", " calibrate_keithley_smu_v,\n", " calibrate_keithley_smu_v_single,\n", " save_calibration,\n", " setup_dmm,\n", ")\n", "\n", "# QCoDeS functions for running a measurement\n", "from qcodes.dataset import do1d, load_or_create_experiment\n", "\n", "# Keithley SMU driver\n", "from qcodes.instrument_drivers.Keithley import Keithley2614B\n", "\n", "# Keysight DMM driver\n", "from qcodes.instrument_drivers.Keysight import Keysight34470A\n", "\n", "# %% Load the SMU instrument\n", "smu = Keithley2614B(\"smu\", \"USB0::0x05E6::0x2614::4347170::INSTR\")\n", "\n", "# %% Load DMM instrument\n", "dmm = Keysight34470A(\"dmm\", \"TCPIP0::10.164.54.211::inst0::INSTR\")\n", "\n", "# %% Calibrate both channels\n", "calibrate_keithley_smu_v(smu, dmm)\n", "for smun in smu.channels:\n", " smun.volt(0)\n", "\n", "# %% Calibrate single channel in specific range, e.g. SMU A\n", "smun = smu.smua # e.g. SMU A\n", "\n", "setup_dmm(dmm)\n", "dmm.range(1.0)\n", "calibrate_keithley_smu_v_single(smu, smun.short_name, dmm.volt, \"200e-3\")\n", "smun.volt(0)\n", "\n", "# %% Check calibration by measuring voltage with DMM while sweeping with SMU\n", "smun = smu.smua # e.g. SMU A\n", "\n", "smun.sourcerange_v(200e-3)\n", "smun.measurerange_v(200e-3)\n", "smun.volt(0)\n", "smun.output(\"on\")\n", "\n", "dmm.aperture_time(0.1)\n", "dmm.range(1)\n", "dmm.autozero(\"OFF\")\n", "dmm.autorange(\"OFF\")\n", "\n", "load_or_create_experiment(\n", " \"MeasurementSetupDebug\", \"no_sample\", load_last_duplicate=True\n", ")\n", "\n", "do1d(\n", " smun.volt,\n", " -0.1e-3,\n", " 0.1e-3,\n", " 101,\n", " 0.3,\n", " dmm.volt,\n", " smun.curr,\n", " measurement_name=f\"{smun.full_name} calibration check\",\n", ")\n", "\n", "smun.volt(0)\n", "smun.output(\"off\")\n", "\n", "# %% Save calibrations\n", "save_calibration(smu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "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": "3.8.8" }, "nbsphinx": { "execute": "never" } }, "nbformat": 4, "nbformat_minor": 2 }