"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Qcodes example with Keysight B1500 Semiconductor Parameter Analyzer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instrument Short info\n",
"Here a short introduction on how the B1500 measurement system is composed is given. For a detailed overview it is strongly recommended to refer to the *B1500 Programming Guide* and also the *Parametric Measurement Handbook* by Keysight.\n",
"\n",
"### Physical grouping\n",
"The Keysight B1500 Semiconductor Parameter Analyzer consists of a *Mainframe* and can be equipped with various instrument *Modules*. 10 *Slots* are available in which up to 10 *modules* can be installed (some *modules* occupy two *slots*). Each *module* can have one or two *channels*.\n",
"\n",
"### Logical grouping\n",
"The measurements are typically done in one of the 20 measurement modes. The modes can be roughly subdivided into \n",
" - Spot measurements\n",
" - **High Speed Spot Measurements**\n",
" - Pulsed Spot measurement\n",
" - Sweep Measurements\n",
" - Search Measurements\n",
"\n",
"The **High Speed Spot (HSS)** Mode is essentually just a fancy way of saying to take readings and forcing constant voltages/currents. The *HSS* commands work at any time, independent of the currenttly selected Measurment Mode.\n",
"\n",
"With the exception of the *High Speed Spot Measurement Mode*, the other modes have to be activated and configured by the user."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Qcodes driver info\n",
"As can be seen already from the instrument short info, the instrument is very versatile, but also very complex. Hence the driver will eventually consist of two layers:\n",
" - The Low Level interface allows one to utilize all functions of the driver by offering a thin wrapper around the FLEX command set that the B1500 understands. \n",
" - A Higher Level interface that provides a convenient access to the more frequently used features. Not all features are available via the high level interface.\n",
"\n",
"The two driver levels can be used at the same time, so even if some functionality is not yet implemented in the high-level interface, the user can send a corresponding low-level command.\n",
"\n",
"### Integer Flags and Constants used in the driver\n",
"Both the high-level and the low-level interface use integer constants in many commands. For user convienience, the `qcodes.instrument_drivers.Keysight.keysightb1500.constants` provides more descriptive Python Enums for these constants. Although bare integer values can still be used, it is highly recommended to use the enumerations in order to avoid mistakes.\n",
"\n",
"### High level interface\n",
"The high level exposes instrument functionality via QCodes Parameters and Python methods on the mainframe object and the individual instrument module objects. For example, *High Speed Spot* Measurement commands for forcing constant voltages/currents or for taking simple readings are implemented.\n",
"\n",
"### Low level interface\n",
"The Low Level interface (`MessageBuilder` class) provides a wrapper function for each FLEX command. From the low-level, the full functionality of the instrument can be controlled.\n",
"\n",
"The `MessageBuilder` assembles a message string which later can be sent to the instrument using the low level `write` and `ask` methods. One can also use the `MessageBuilder` to write FLEX complex measurement routines that are stored in the B1500 and can be executed at a later point. This can be done to enable fast execution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Programming Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initializing the instrument"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Markdown, display\n",
"from matplotlib import pyplot as plt\n",
"from pyvisa.errors import VisaIOError\n",
"\n",
"import qcodes as qc\n",
"from qcodes.dataset import (\n",
" Measurement,\n",
" initialise_database,\n",
" load_or_create_experiment,\n",
" plot_dataset,\n",
")\n",
"from qcodes.instrument_drivers.Keysight import KeysightB1500\n",
"from qcodes.instrument_drivers.Keysight.keysightb1500 import MessageBuilder, constants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"station = qc.Station() # Create a station to hold all the instruments"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[spa(KeysightB1500)] Could not connect at GPIB21::17::INSTR\n",
"Traceback (most recent call last):\n",
" File \"C:\\Users\\jenielse\\source\\repos\\Qcodes\\qcodes\\instrument\\visa.py\", line 116, in _connect_and_handle_error\n",
" visa_handle, visabackend = self._open_resource(address, visalib)\n",
" File \"C:\\Users\\jenielse\\source\\repos\\Qcodes\\qcodes\\instrument\\visa.py\", line 139, in _open_resource\n",
" resource_manager = pyvisa.ResourceManager()\n",
" File \"C:\\Users\\jenielse\\Miniconda3\\envs\\qcodespip310\\lib\\site-packages\\pyvisa\\highlevel.py\", line 2992, in __new__\n",
" visa_library = open_visa_library(visa_library)\n",
" File \"C:\\Users\\jenielse\\Miniconda3\\envs\\qcodespip310\\lib\\site-packages\\pyvisa\\highlevel.py\", line 2899, in open_visa_library\n",
" wrapper = _get_default_wrapper()\n",
" File \"C:\\Users\\jenielse\\Miniconda3\\envs\\qcodespip310\\lib\\site-packages\\pyvisa\\highlevel.py\", line 2858, in _get_default_wrapper\n",
" raise ValueError(\n",
"ValueError: Could not locate a VISA implementation. Install either the IVI binary or pyvisa-py.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connected to: Agilent Technologies B1500A (serial:0, firmware:A.04.03.2010.0130) in 0.11s\n"
]
},
{
"data": {
"text/markdown": [
"**Note: using simulated instrument. Functionality will be limited.**"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Note: If there is no physical instrument connected\n",
"# the following code will try to load a simulated instrument\n",
"\n",
"try:\n",
" # TODO change that address according to your setup\n",
" b1500 = KeysightB1500(\"spa\", address=\"GPIB21::17::INSTR\")\n",
" display(Markdown(\"**Note: using physical instrument.**\"))\n",
"except (ValueError, VisaIOError):\n",
" # Either there is no VISA lib installed or there was no real instrument found at the\n",
" # specified address => use simulated instrument\n",
" b1500 = KeysightB1500(\n",
" \"SPA\", address=\"GPIB::1::INSTR\", pyvisa_sim_file=\"keysight_b1500.yaml\"\n",
" )\n",
" display(\n",
" Markdown(\"**Note: using simulated instrument. Functionality will be limited.**\")\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'spa'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station.add_component(b1500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## High Level Interface\n",
"\n",
"Here is an example of using high-level interface.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Identifying and selecting installed modules\n",
"As mentioned above, the B1500 is a modular instrument, and contains multiple cards. When initializing the driver, the driver requests the installed modules from the B1500 and exposes them to the user via multiple ways.\n",
"\n",
"The first way to address a certain module is e.g. as follows:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1 # first SMU in the system\n",
"b1500.cmu1 # first CMU in the system\n",
"b1500.smu2 # second SMU in the system"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.phase_compensation_mode()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The naming scheme is - `b1500.`, where number is `1` for the first instrument in its class, `2` for the second instrument in its class and so on. (*Not the channel or slot number!*)\n",
"\n",
"Next to this direct access - which is simple and good for direct user interaction - the modules are also exposed via multiple data structures through which they can be adressed:\n",
" - by slot number\n",
" - by module kind (such as SMU, or CMU)\n",
" - by channel number\n",
"\n",
"This can be more convenient for programmatic selection of the modules."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instrument modules are installed in slots (numbered 1-11) and can be selected by the slot number:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.by_slot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All modules are also grouped by module kind (see `constants.ModuleKind` for list of known kinds of modules):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.by_kind"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, let's list all SMU modules:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.by_kind[\"SMU\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lastly, there is dictionary of all module channels:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For the simulation driver:\n",
"# Note how the B1530A module has two channels.\n",
"# The first channel number is the same as the slot number (6).\n",
"# The second channel has a `02` appended to the channel number.\n",
"b1500.by_channel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note: For instruments with only one channel, channel number is the same as the slot number. However there are instruments with 2 channels per card. For these instruments the second channel number will differ from the slot number.**\n",
"\n",
"**Note for the simulated instrument: The simulation driver will list a B1530A module with 2 channels as example.**\n",
"\n",
"In general, the slot- and channel numbers can be passed as integers. However (especially in the case of the channel numbers for multi-channel instruments) it is recommended to use the Python enums defined in `qcodes.instrument_drivers.Keysight.keysightb1500.constants`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Selecting a module by channel number using the Enum\n",
"m1 = b1500.by_channel[constants.ChNr.SLOT_01_CH1]\n",
"\n",
"# Without enum\n",
"m2 = b1500.by_channel[1]\n",
"\n",
"# And we assert that we selected the same module:\n",
"assert m1 is m2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Enabling / Disabling channels\n",
"\n",
"Before sourcing or doing a measurement, the respective channel has to be enabled. There are two ways to enable/disable a channel:\n",
" - By directly addressing the module\n",
" - By addressing the mainframe and specifying which channel(s) to be enabled\n",
"\n",
"The second method is useful if multiple channels shall be enabled, or for programmatic en-/disabling of channels. It also allows to en-/disable all channels with one call."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Direct addressing the module\n",
"b1500.smu1.enable_outputs()\n",
"b1500.smu1.disable_outputs()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Enabling via the mainframe\n",
"\n",
"# enable one channel\n",
"b1500.enable_channels([1])\n",
"\n",
"# enable multiple channels\n",
"b1500.enable_channels([1, 2])\n",
"\n",
"# disable multiple channels\n",
"b1500.disable_channels([1, 2])\n",
"\n",
"# disable all channels\n",
"b1500.disable_channels()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perform self calibration\n",
"\n",
"Calibration takes about 30 seconds (the visa timeout for it is controlled by `b1500.calibration_time_out` attribute)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.self_calibration()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performing sampling measurements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section outlines steps to perform sampling measurement. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set a sample rate and number of samples. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Number of spot measurments made per second and stored in a buffer.\n",
"sample_rate = 0.02\n",
"# Total number of spot measurements.\n",
"nsamples = 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign timing parameters to SMU. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.timing_parameters(0, sample_rate, nsamples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Autozero is generally disabled for sampling measurement. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.autozero_enabled(False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set SMU to sampling mode. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.measurement_mode(constants.MM.Mode.SAMPLING)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SMU is configured with by assigning voltage output range, input output range and compliance. While forcing voltage, current should be the compliance and vice versa.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.source_config(\n",
" output_range=constants.VOutputRange.AUTO,\n",
" compliance=1e-7,\n",
" compl_polarity=None,\n",
" min_compliance_range=constants.IOutputRange.AUTO,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the averaging to 1 otherwise the measurement takes 10 times more time. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.use_nplc_for_high_speed_adc(n=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the voltage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.enable_outputs()\n",
"b1500.smu1.voltage(1e-6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now ready to start the sampling measurement. We first initialize the database and create-new/load-old experiment. Then we register our dependent and independent parameters and start the measurement. \n",
"\n",
"**Note** that the default values of label and units are not defined for the parameter sampling measurement trace. Hence we first set them according to what is being measured: in this case we will measure current in A. It is important to set the label and the unit before the measurement in order to have this information when looking at the acquired data, for example when plotting it with `plot_dataset` as shown below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.sampling_measurement_trace.label = \"Current\"\n",
"b1500.smu1.sampling_measurement_trace.unit = \"A\"\n",
"# Automatic assignment of the label and unit based on\n",
"# the settings of the instrument can be implemented\n",
"# upon request."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"initialise_database()\n",
"exp = load_or_create_experiment(\n",
" experiment_name=\"dummy_sampling_measurement\", sample_name=\"no sample\"\n",
")\n",
"meas = Measurement(exp=exp)\n",
"meas.register_parameter(b1500.smu1.sampling_measurement_trace)\n",
"\n",
"with meas.run() as datasaver:\n",
" datasaver.add_result(\n",
" (\n",
" b1500.smu1.sampling_measurement_trace,\n",
" b1500.smu1.sampling_measurement_trace.get(),\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thanks to the `label` and `unit` set above for the `sampling_measurement_trace` parameter, the `plot_dataset` function is able to produce a plot with a useful label for the vertical axis, see below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_dataset(datasaver.dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check compliance: For the values which are compliant the output is one and for others it is zero. A quick to visualize of your measurements are compliant is to plot the compliance data and look if any value of zero."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_compliance = b1500.smu1.sampling_measurement_trace.compliance()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(data_compliance)\n",
"plt.xlabel(\"Measurements\")\n",
"_ = plt.ylabel(\"Compliance status\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The channel number of the measured data can be obtained in the following way. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_channel = b1500.smu1.sampling_measurement_trace.data.channel\n",
"data_channel[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to know the type of the measured data, for ex 'I' or 'V' the following method can be used."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_type = b1500.smu1.sampling_measurement_trace.data.type\n",
"data_type[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The measurement status can be obtained using:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_status = b1500.smu1.sampling_measurement_trace.data.status\n",
"data_status[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variable 'data_status' is a list of strings of measurement status for each data point. One can look at the meaning of the statuses in `constants.MeasurementStatus` class. It enlists meaning of all possible measurement status. For example: in case the measurement status is 'C' its meaning can be found as following."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"constants.MeasurementStatus.N"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"constants.MeasurementStatus.C"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CV Sweep"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MFCMU has two modes of measurement. The first is spot measurement and this here is sweep measurement. As the name suggest sweep measurement execute the measurement once for the whole list of voltages and saves the output in the buffer untill measurment is completed.\n",
"\n",
"The function below sets up properly the parameters to run the sweep measurements. Look at the docstring of ``setup_staircase_cv`` to know more about each argument of the function. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.enable_outputs()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"b1500.cmu1.setup_staircase_cv(\n",
" v_start=0,\n",
" v_end=1,\n",
" n_steps=201,\n",
" freq=1e3,\n",
" ac_rms=250e-3,\n",
" post_sweep_voltage_condition=constants.WMDCV.Post.STOP,\n",
" adc_mode=constants.ACT.Mode.PLC,\n",
" adc_coef=5,\n",
" imp_model=constants.IMP.MeasurementMode.Cp_D,\n",
" ranging_mode=constants.RangingMode.AUTO,\n",
" fixed_range_val=None,\n",
" hold_delay=0,\n",
" delay=0,\n",
" step_delay=225e-3,\n",
" trigger_delay=0,\n",
" measure_delay=0,\n",
" abort_enabled=constants.Abort.ENABLED,\n",
" sweep_mode=constants.SweepMode.LINEAR,\n",
" volt_monitor=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the setup function does not output any error then we are ready for the measurement. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting experimental run with id: 2330. \n"
]
}
],
"source": [
"initialise_database()\n",
"exp = load_or_create_experiment(\n",
" experiment_name=\"dummy_capacitance_measurement\", sample_name=\"no sample\"\n",
")\n",
"meas = Measurement(exp=exp)\n",
"\n",
"meas.register_parameter(b1500.cmu1.run_sweep)\n",
"\n",
"with meas.run() as datasaver:\n",
" res = b1500.cmu1.run_sweep()\n",
" datasaver.add_result((b1500.cmu1.run_sweep, res))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ouput of the ``run_sweep`` is a primary parameter (Capacitance) and a secondary parameter (Dissipation). The type of primary and secondary parameter depends on the impedance model set in the ``setup_staircase_cv`` function (or via the corresponding ``impedance_model`` parameter). The setpoints of both the parameters are the same voltage values as defined by ``setup_staircase_cv`` (behind the scenes, those values are available in the ``cv_sweep_voltages`` parameter). "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([,\n",
" ],\n",
" [None, None])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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HoPrwXVAxr64hqV8y8wKAv4a6J4ehWPp3jd0fCPXOLEJd+8eZ+VL9W6f97aP62BcT0QLUc31M8i6x1/RtqP7yVf1e3wzg2Rl3/zmUB2sfEYVZk+B7AB5MRFnc2ICatG+DYpg3QY3DcXmujwLwGyJahLoff8PM9yC9764EX4LyUO6DClf8dcx2r4SaIN0K1Q++gWDY5yVQLtVEULvNyREHIvosFHv7SZePey6UoioLG8vRQ5CKVR+AUi5u7+F5Pg8lMHpnr86RIwcRvQHAQ5j5LUe6LSsBEV0KNTZ+dpXHeR6AVzDzn6Ztu9aSUY8omPnPj3QbcvQcbwZwTS8NYo4c/QIzf/pIt2EQwMzfg2LOqciNYo4cGkS0E0oQ8ILQ97cgKEAQvJGZv9KHpq1JENFJUK6sKDyEme/rZ3ty5MiC3H2aI0eOHDlyaOSrZOTIkSNHjhwax5T7dMOGDXzKKacc6WbkyJEjx5rCtddee5CZV5SDvtZwTBnFU045Bdu2bTvSzciRI0eONQUi6rTy1JpF7j7NkSNHjhw5NHKjmCNHjhw5cmjkRjFHjhw5cuTQyI1ijhw5cuTIoZEbxRw5cuTIkUMjN4o5cuTIkSOHRm4Uc+TIkSNHDo3cKObIkeOYxV0HFvHrHYf6dr49s7X0jXIcUeRGMUeOHMcsPnHpDrzjWzf25Vy375vH49//c9yyZ64v58uxMuRGMUeOHMcsmo6Lesvty7kOLqi1ymeWmilb5jiSyI1ijhw5jlk4rgvb7Y9RbOnzOG6+MtEgIzeKOXLkOGZhO4ym3R+jaDvKGLr5cn0Djdwo5sixhsDM2Hlw6Ug346iBywy7T8zNdoQp9uV0OVaI3CjmyLGG8MvtB/HUD16aqxi7BNtlj8H141xA7j4ddORGMUeONYRDSw0wA7PLra4f+56DS7jw6vu6ftxBhuMymo4L7oNLU2KXuft0sJEbxRw51hBathpQeyEO+ea1u/COb93UFwMxKHD6yN5aTs4U1wLWhFEkoguI6AAR3Wx8dx4R7Sai6/W/5xzJNubI0Q80dUCq1YPAVL3lAEDfYmyDALnWVh9cqLnQZm1gTRhFAJ8H8KyI7z/MzGfrfz/sc5ty5Og7bM8odn9g7aXBHVQIa2v1IS3DzlMy1gTWhFFk5ssBzBzpduTIcaQhxrAX4hBJTegHaxoUeEyxD2kZuft0bWBNGMUE/CUR3ajdq1NRGxDRG4hoGxFtm56e7nf7cuToKoTR9ILZNOxjjym6rsRo++E+zYU2awFr2Sh+AsDpAM4GsBfAB6M2YuZPM/M5zHzOxo0b+9m+HDm6Dk9o00Om2K8UhUGAGMN+JPD7KRk9P1WOVWDNGkVm3s/MDjO7AD4D4NFHuk05cvQaEpeyezCyHotM0ZH72Rf1qY4p5kxxoLFmjSIRnWB8/CMAN8dtmyPH0QIRwzR7YLiOZaFNLyYZYXjq0zymONAoHukGZAERXQjgXAAbiGgXgH8CcC4RnQ2AAewE8MYj1sAcOfoEu6dCG5WScSwJbcQo9mKSEUZeEHxtYE0YRWZ+ScTXn+t7Q3LkOMIQFteL5P1j0X1qu72bZITh5OrTNYE16z7NkeNYhLC4nuQpHoNG0fWS9/sotMljigON3CjmyLGG4DHFXsQU7f6JTgYF/axo4wltjqH7uxaRG8UcOdYQWv2oaNOn9QUHAU4/mWIutFkTyI1ijhxrCDKw9iJ533OfHkODtrgyexGjDcMT2uTu04FGbhRz5FhDaHru0+4PrJ7Q5lhiio4k7/exIPgxNOlYi8iNYo4cawh2X2KKx45R9NSn/SwInjPFgUZuFHPkWEPw1Kc9YBtiFJvHYJ5iP1Iy/ILgPT9VjlUgN4o5cqwh9Ep9ysyGa/bYGbWFtfUjeT8vCL42kBvFHDk0Wo6L+2eWj3QzEtEr9WnDiCMeK3mKzNxXpugXBM+N4iAjN4o5cmh874Y9ePqHLsNSwz7STYmF3aMUApMpHStl3kzj1M+UjNwoDjZyo5gjh8ahxSYatovlpnOkmxKLXi3v1DwGmaIpeOlPRZvcfboWkBvFHEcMH77kTvzm7kNHuhkemmsg5uMxxS6rJU2jeKyspxhkiv0U2mQ7l+My/vZr1+OWPXO9bFaOEHKjmOOI4VOX78APb9p7pJvhwS+2PbhGodWjPEXTKPZDdDIIMJ9zX5aO6pApHl5u4lvX7cYV2w/2slk5QsiNYo4jBtcFFhuD46oUw+AMMFPylo7qMlNs9JgpXnLrfnztmvu7ftzVwB3wmKI8k6UBducfjciN4jGCPbO1gRuUHGYsNwdH1NLLZZm6BW+R4S5XYOl1TPGCK+7BZ6+4u+vHXQ1MptiP0nZ+QfBs2zdayhguD7Dw62hEbhS7jHrLwf75+pFuRhu+ce0uvO2bN6LeSp91XnLrflyzc6an7RE5/CDNgjuN+RwJ9MpwNx3/OfSirureudrAuaWdvrtPdZm3jO5TYYrLGd7ZHN3DETWKRHQBER0gopuN7/6NiG4nohuJ6NtENBmz704iuomIrieibf1rdTK++OudeNZHLj/SzWjDXK0FAGi00l/+D158Bz512Y6etkfGo0GaBQsLG+QyXJ77tJd5il1moa7L2DNXHzgBT7+FNp26T4W9D9I7cizgSDPFzwN4Vui7SwA8jJkfDuBOAO9I2P+pzHw2M5/To/Z1jIOLTRxebg0c25gXo+ikzzqbtovFHr+Icn/M89y0aw4v/PivMrHZXqBX6Q7dhLe8U5eZTSCm2GWmeGipiabtDtw70e88xVaHk648pnhkcESNIjNfDmAm9N3FzCwj5VUAtvS9YauAVz9ywFYaWKirW5qlXU2n97l64kIyz3PDrllcd98sphcaPT13HNbCIrB2jxSyvYwp7pmt9eS4q4Xdb6GNuE8zC23Uu1HLjWJfcaSZYhpeC+BHMb8xgIuJ6FoiekPcAYjoDUS0jYi2TU9P96SRJjwhxIANAPN1zRQzGEXb4Z4zRRkgTKGNp/48QkZp0FMyHJc9t3PXK9oEjGJ3r3/vnDKKgzbZcAxG3J+C4J31bwl1LA2QGO1YwKqNIhGNEJHVjcaEjvuPAGwAX4nZ5AnM/AgAzwbwF0T05KiNmPnTzHwOM5+zcePGbjezDYPKFMUoZmlXy3Gx3ONUiSj36ZGO6Ymic9AGb4FpCLttuKRfEHXf4O6eVcKzQWOKZnP6MYl1Vii0yZlif9GxUSSiAhG9lIh+QEQHANwOYC8R3aJFMg9cbaOI6FUAngvgZczRPYiZ9+j/DwD4NoBHr/a83YC//M5gDQCduE9bjtvz2am4kOotP9Z0pJlic8BTMkyj0m21pFz7aLnYddYk7tNBm2zYfWaKnecpKmOYM8X+YiVM8RcATocSwBzPzFuZeROAJ0HFAN9PRC9faYOI6FkA/h7AHzJz5JIFmp2Oyd8AngHg5qht+w0ZuAaOKdayu09bDmOpYSNmPtIVmC5KcaEeaaGLrDg/oDYxcF+67eKVnLiRSrHrEzpxnw6aWzqQktGHh97yFhnOtr2vPs2ZYj+xEqP4dGZ+LzPfyMxeT2LmGWb+JjP/MYD/zXIgIroQwK8BnEFEu4jodQA+BmAMwCU63eKTetsTieiHetfjAFxBRDcAuBrAD5j5xyu4lq6jl+7Tz1x+N/7l+7d2vB8zY74Dpmi7LlzOZkBXCtOFJGKbXtQeXWrY+IuvXIebdqXXjxz05P2g+7Q3THG4YnWdhYr7dJCNYq8XVnZchnTr7EIbbRRz92lfUex0B2ZudWMbvd1LIr7+XMy2ewA8R/99N4Czspyj32j2kCn++u5D2HW48/X+ai3Hd1GmpGQwsxevWmrYqJa6Hi4GEByQFhs2joPBFLs4eH7qsh34wU17ceaWCZy5ZSJx20FXn0rVFaLe1T4drRS7Hq803afMDCLq6vFXin4k7zdsB1/btgt/8ghfRN+p+1TeX6swGPftaMegq0/XHHoZU7RdXhF7m6/5MYm05H3TIC310G1jDgziHpJBoFtGadfhZXzqclVabHY5fZ7W9OqKDqhR1M9+qGT1RH1KBFS7fOyG7WB6oYFyUQ01g3Rv+5GSceWOQ3jXRTdj271+5lnmPEXjXa3lVW36htwodhm9jCnajrui44ryFEg31ubg0MsAv2n45DyNLgttvnDlTjADI2XLq+iThGaXjXK3Ic9muNx9o9iwXZStAspWoavH3j+nck63Tg0BGKx7K27MaqnQs4o2Eqs1+1+n7lMAA1Uj+GhHbhS7jF7mKa6UKS4YRjFtf3Nw6OWLaM6WZaV7333anXt3aKmJjWMVHD9R9YRGSWgNOlN0ZBC3ui+0sV2UiwWULOrqsWeWmwCATWNVAIOVlmG7/v3M2q7lpo3L75zGHfsWMhk38T6I+ts8bxrEcwLkYpt+oiOjSERvIqLPENGLiej7RPTmXjVsrULqRvaCKTour4wpmu7TVKPo/97LZZ3cAFPUQpsuqz+btotKqYCJoRJma83U7eXas87k+w2TKXY9pui4qBQLKFqFrvZdSZCvlgr68+DcW2lLtZj9fn716vvxyguuxjM/cjk+fuldqduLy9ucmGYVkjUDTDE3iv1Cp0KbpwH4MwC/ZOYnijI0h49eCm1sxw3MHrMi4D5NaZc5OPSyEHEgJUOYYpfVnw3bRaVoYXK4jAML6SuXDHpFG7kvQ+Vi11eyaOp7VbYKXb1+22C3QH8Kb2eFGMWhDtzRs8tNEKl8zn0ZVsOR45oT007XUwRy92k/0an79JBOpv+A/nxkilQOMMTo9MJNZLtKGdopk5mvt5dSi0Mwptgfoc1iyH3arZSMpnYJTgyVMglt/Jjm4Lj4TEjFneGSBebusi65V0WLMvXdd150E75w5c7U7aSNleLgMUXbaFvWScZy08FQycJwxcq0mojcS5MprsQo5kXB+4dOjeJHAYCZv6c/f6u7zVn76C1TlLSKzo5txtPSmGbAKPaQKUbmKXY5eb9hO6hoo5hFaLNWmOJwWVhXd1WiZauAklXIdP9/cfs0vn/jntTtfMPT/TavFo4RU8za55ZbDobLFsrFQqb3MCqmmL3Mm/+u1nKm2Dd0ZBSZ+XYAIKIN+vNlvWjUWoa89I2eMEV97AxrIpqYr7cUCyhQuvs0QhXaC0Sdx6t92iWjpNynyigu1O3U4w76IsPSt6o9MIpNQ2iTZbBv2C7u3L+YWvXIY4qDGFNkU32a7V7Wmw6qJQuljLFXz32qjWKxQB0VBB+rqghXL9OjcgSxUvXpBV1txVGEXla0EUOSZU1EEwt1G+PVIirF9Bc5ENzvk9BGzuPVPu2i+1SMIoBEBarjsjdYDdLAbUKM9rCOz3VTbCNCG8UUsxhFB3O1Fg4uJguYTIWn+jxITFFEQFbmWOdyUzNFKyNTDAltKsUCsnavhu1i3UhZnzdniv3CSo1iXlohBr3NU9RGsVOmWGthvFpCuVhIVZ/aEbG+XiCQpxiKKXaXKVqYHFZGMcmFajKFwTWKIfdpFw2MF1MsZMvZk2e1/cBC4nZieCqDmLwvIqBidqFNraViilkmmIAZU1R9vFKyMvevpu1icliMYs4U+4WVGsXB6dkDBNf1S6T1InYiL1OnuYrzdRtjQ8oodiK06WmeYh+S902hDQDMJhhFc9Y/SAO3CRnEh8rFwOduwMtTLKYLbZj9fNm7DiwmbiuHqvaA3a4WfkwxWxwVUMs4DZWV+zTLOy79aqHhM8VOyrxN6r6bC236h5wpdhHmzL037tOVsdCFeku7T60OK9r0UH0aIbSRgbZbRskU2gApTNEefKYoz26o1KOYolVAqZA+2JtMcvv+ZKNoDzBT9GOK6r3IsipMreVguFzMNMEE/LxlSclQ7tPs6tNqqYChkpULbfqIlRrFd3S1FUcJzJckS7xhod5CvYOahl5MscNcxaD7NE192u7W7AXE8IyULSMlo7tl1oT9ZHOfmsWhB2fgNtHmPu1mTNF2UdECEjcl3cPsQ+lMUYQ2lv48SDHFYLwzS79bbtoYEqFNhnc8nJJRKWZ3n4r7f6Ri5Uyxj+h4lQwNm4j+HsBmKFfqHgDfZebbutayNQhzkMoyi3z5567GOSdP4V3PfUim43spGStxn1aLShyQmryvfh+tFN/TK8sAACAASURBVHsqtJGBYaxa8oU2XVafitBmXJjicrwoJBhTHJyB24QXAyt3X7QitU+LlnICtRwXViF6hRTpQ0TA9hSjaOYCquMOzoQjqrBAMWVRmHrLxVBZMctOYorSpSulDphiS3k6hsvFnhbSyBFEx0xRG8OvQrlQrwZwjf77QiJ6e3ebt7ZgviRZ4n67D9ewP0NVDIGXkrES9+lQCZVSutBGXuKJoVJfCoKPDxX9lIweCW2yuE/N+5JV/bp7toZnfvhy3LZ3fnUNzQiPKfZIfVouqoLgQLKbU+7V6RtHcXCxgcNL8ZONQU7ed42UDCCbcEmYYvY8xeA2ZauTmKIqUzhctnKhTR+xEvfp6wA8ipnfz8xf1v/eD+DR+reug4guIKIDRHSz8d06IrqEiLbr/6d6ce5OYBrFLPGeWtPuaGBbidCmYTuot1yMa6aYtSD4xFCpL8n741V1Httxvdl0N+JOtuPCcRnlYgGVooWhkpVY1aa1AqHNjgOLuGP/At7zvVsyxaNWCy8loxfJ+5qVeEwxoZ9IHzptwwgAYO9c/MQunJIxSMn7bekiGd7FWstPycgktAndx0opu1GU0nu5UewvVmIUXQAnRnx/gv6tF/g8gGeFvns7gJ8x8wMB/Ex/PqIwZ4VprhVmxnLLyewCMxf/7cR9KlLwsWpn6tPJ4VJP4xi25z4tYqnpBO5dNwpyy/GEoaRVtQm4TzNOVMQ4XHX3DC6+df9Km5oZ0sYhz33amzxFIJk1SR8SBl5PiFM7oecwSEzRLwgurt3kd8N1GfWWi2rJyhSKiDpmpWh1lKdYLhYwUin21GuTI4iVGMW3APgZEf2IiD6t//0YyjC9pbvNU2DmywHMhL5+PoAv6L+/AOAFvTh3JwgIbVJemIbtgjl7jMV8kRq2g4btZGJyYhTHh5T6NDVPUbdncrjU0ziG7z4toWm7gZlwNwZ7yeWUxW0nh7MbxU6X9hkpW/jcFfestKmZIfFej3V1UeFsVrQBkvulXLdnFBPEYu1McQCNYkYWKwv9emXeMhnF4PVmTclwXfYmKkp9Gn+P987V8uT+LqJjo8jMPwbwIADvAfATABcDOA/AGcz8o662LhnHMfNe3aa9ADZFbUREbyCibUS0bXp6uqcNMl+qtHiDdPKsTLEVYqHn//B2vPxzv0ndT6q4jFdLOuE4+HLdsW8Bf/6Fa7yBrunFFMtYajo9W0bJdJ8CCMSlulEQ3GeKasAbHyol5ikGYoodlOECgIdunsC9h5ZW2tTMaDqs65Nqw9WlZyOua6l9CiQbXDEGImBKKiYxyDFFMdjljCIg0yiqPMX0a2lnioVMMWuz/6YxxRd+/Ep88tIdqcfMkQ0rSslgZpeZr2LmbzLzN/TfDhG9ptsNXC2Y+dPMfA4zn7Nx48aenqsT96m8YFlnzuZg0rBd7Dq8jHsPLafuJ8tGee7T0Et65Y6D+OltB7wV0m0n6BqrdZAy0gmkGeNDSgB92Ij3dUNAIgO16T5NKvMWSMnocBWD0zeO4MBCoye5qSZsx0XRIhQLBe9zNyDPuFqyUPSENukxxZUwxUEr81YskDcRSLufMpGtlrIzxbaYYtHKNNE0++9QOZ4pui5j33wdBxPETjk6w0rzFOPwni4fLwn7iegEAND/H+jjuSMhs+sCpTNFcRdmHdjsULrHUkPVnkwTeATdp4W2Wb2IT4Qptgz3KdC7ouCS9iBMcWap0fbbaiDX47lPU5aPMplRVnYs5zhtwyiYlRurl2g5LkqBtInusC4pNj1SKaKsj91MWBbJZ4pqQpMYUwwxxUHKAXVcoGAYxVTvjscU1X3KkvDfxhRL2Zii9K1KqYDhkhVbEHyhYYO589KPOeKxkpSMG2P+3QTguB60MQ7fBfAq/ferAHynj+eOhKyMMVIppjNFz32abZAwZ9gN28FyU636kFaf1HSfClO85Nb9ePT7fopa0/HibDL7F4GFV16qR7mKMlYI2zhkzHSTBo0DC3W84nO/wcHF5KU85XpkMB6rlgJr2oWxspiiVmFuVCrM3Yd7bBRdRskiI22iOwOh9KGRiuWz0ESmGI4pxm8bdlEOkvvUZ4pqIpBmsGUiO1QuZHa5hg1tpVgAMzxj+p3rd+Ozv7y7bT+//ypWGhfvlPe7bhS+6IcS+mjGSpjicQBeCeB5Ef8Oda9pPojoQgC/BnAGEe0iotcBeD+A3yei7QB+X3/uOZgZF1xxD2YjEsGFbYxmMYqrdJ+KMjRt8Vzffeon79+2dx4HFho4sFBvN4p2kCku1nvEFNlP/QCCMcUko3TV3TP45faDuGnXXOLx5Xpk8Kqm5Giag1dWpiqz81N1asKu2R4bRVuYYja15Ccv24F/+s7NidsAfo3b0UoRpQxKTLmPwvKT3KeO68Iy2Fg3i5ivFrbLwbZldJ8OlYpev4pil/vm6vjb/70e9ZbTthBxuHrOt67bjQuvvq/tGOakrlwswHajFxeX91v64h9/4kr8x8/uSryOHMlYSUWb7wMYZebrwz8Q0aWrblEEmPklMT/9Xi/Ol4T7Z2r45+/fivGhEv7kkVsCv8kLMlopZnCfqoEoq/vUFFUo96naf67WwtaE/RbqNgoEjJSLXvK+GNKZpaZn3GX2b7suiIATJ4cAKJfgmVsmMrWxE4hUX8QaM0u+cU9yX96nBS2HE6rTAL6LT4Q2laIFWy8PZRXaS/fK9mWrkJkpNh0HxQJhy9QwiPrAFMV9WsjmPr1i+8FMLl2fKRa9e5+sPg3HFJOZolUgr82DxRR12zK6o2stdZ+kIDigJ8KV4HbX7JzBt367G6994qmRQhtATQqLUEYtarJmuv9N9241VGVIwiOy/c5DS7h3pveir6MZK1Gfvo6Zr4j57aWrb9JgY7kVb8xaPXSfmrlzjZBRTMJ8rYWxagmFAqFsqbqLEr87vNxsY4pNPfBunRoGAOzq0UAvlzMx1B5TTLonIi5KY8jhmKI3s495Li2v5FcHFUdarjeT3zRWwe5eM0WXUbRMYUhyO+MG3DC8mGI5G1MMq0+TmKLrMooFXxw0aCkZxUJ2d3StqX6XlAwgmimKerphq/zbsYriHkTwWL6car4WZxR9plhJeCbzofe33nJ6Lvg62rGSmGLqChlZtlmrkFlxVNyruSL3aVamGI4pZnWf2t7q3fIiH1hQBmhmqeWlKUh7bYdRKhAmh0sYKVu4/3C6wnUlEEbiGUXjOpKM0r0z2iimTAbCMUX53yxm/dGfbsfNu5Ub1i+2XewopiiFrjdPDmFPH9ynZn3StEF8LmbADWMpEFNMj6/JMatFlcSeJLQRpmhZwhQHZ8D2mWI296l4d6QgOBA9yRKjWGu6aDmuN3koFQqw9NAo48d83UYjYlLhq0+TzzXvMUUl+qm33IGqGrQWsZKY4i+I6K+I6CTzSyIqE9HTiOgL8AUwRx1kVhw1cDf1QDJSSV+01Fefdh5TXKjb3sA9W0t2I6plo9RLKYZB6q3OLDXaZpq246JULICIsHXdcM+YorQ/Kk8xySjep5liUnFvwB9ApK5lpRQcWByX8eGf3okf3LQXQLBaTCfqU7mnm6eGe84UbWGKGVnXXK0VOeCGsWTGFDMoMT3XdKmgXPIpeYpFw306SEzRdhkWZW+bvPtDZct77lH3Sb6qtxy0HPYmfkWLUAi5kedrLdQT3KeVUrKox3t/W47v7cmZ4qqwEqP4LAAOVAHwPUR0KxHdDWA7gJcA+DAzf76LbRwoJC2E6zPFUgfu04xM0Xj5zHhauvvU9qTz8nJNa6Z4aKnpp2S0JHmfPVfXlqkh3D/TI6YoxZi1km8mg1Gstxzs0wb9cKr7VGKElv6/EPheBjj532M/Jasjpij3dPPkEPbO1ntW7ABoT8lIikczc6xrLgxhisOGUUxmito1bRVQLVmpeYpWoTCQMUXXZVgWGUYn20RWap8CMUxRX2Ot5aBlu977VywQ9KPTJeOUIWva7akdpqcjiSkuGExRJidZCpXniMdKYop1Zv44Mz8BwMlQYpdHMPPJzPz6KAHO0YQkptjyhDaWl54Rh9WoT01RylwG9emYZmPy8ovLZdfhmmcA5EWyHdfLVdsypZhiFok3M+Nr19yfmPYQdT0WEUYrxUxG0TTQae5Tk82o/5VxlAE9bAzl2a0kpggAmyeraDouplNSRVaDluOiVChkUksuNmy47LvVkrCoY4rDJcso85bMFEua9VRLhWT1qaOYolVIN+RxuGL7Qbzss1d1rViBwHbVBDCLyxjwjWLVcGlG3SdxjdZaDhqO63lDilbBuw8Os6ccBdqL/JtCsaT4pa8+dTw3ds4UV4dVJe8zc4uZ9zLzbLcaNOiQASCqFJnHFKvFyNmfiY6T942B2nQ1psUUF+p2m/tUsPOgr1KTWWbLcb0Yy5apISw27FQ2CigD+7Zv3ogfandkGuR6rAJhpGJ5k4SkpXVEZDMxVEp1n5psBjBjipophhijWj9QiViysnflPtUxxSml1u2Vu1m1kVEqUqb6pPNGKk0aW1xu2BgpW4FE9rSUDLmv1aKVSX1K2k25krq21913GL+66xD2L3R3wiExxazJ+/WWg2qpoERrCcIt6b/Kfer67tOC7z51XcZ8Lf4ZBVIyvIIKyUKbuuHtybFydLuizVEPMR5RL3fLcVEgFYhXn+M7Z03HcbLWrzRnsTMduU9bnvsmbBTvMYyiDAiSIA4opgioNJQ0SFwqzUgLXJdRIICIMFoped8PlePdlyKyOWvrZHahTSmoPpXvxX3tTwZUXdFigTpcGV0dd9NYFYDvmu4FbMdFsaDivVaBEo236UFIM4pLTRvDWiHpG8XkijbCvIfKVkpFG9dz9xatlRlFmTDt7XLM1tExRckdTIu/Ljcd793OIrRZ1NVmPKGN5QttbDfMFIPn9mKKxWTPgMcUbdebnORMcXXIjWKHkAEgKnbU1MnVWWbb8qJnZ4o6RmbUXCwWKFFo47iMhYbd5j4VmCtTeMZBXwMAbF0n7Cc9rihGZj6r+5T9fMHRip97NVy2Yiva3HdoCWOVIk5dP5y4sC0QzDsEDKbYCjLEhuFyKlnK2HS6CCzgC4bSKgytBk2HvWdTLFCiuy9pwA1jseFgVBvFLMrWhu2EmGK6+lS1ubCiMm9y/G4LmaRtcu2LKdWb1FqKwfh8tNBGXaNXYrEaLbQxa/GGxUqm+jTpXHKOesvxmWLK886RjBUZRVJIyhk/auHHFNt/81YvT8mJA3yD5HK2WpsymIyUfQNy3HgVs8stzC43A6xPIAP0uKRkWOa+wYxjP3mfvYHRY4qdGMVaNqOgmKIYRb+GxFDZil3P8N6ZZZy0fhiTw2XM1+1E49WwnYDcXtycMrD4sUVfnFAudsoUfffpqL7HWWKqiw3bU9F2AttxPRaftkrDXMKAG8ZSw8ZIJZ0BCZrGZKBSKiS6T0V9CghT7JzFyPGTFjNeCYTFVksFFAipyy/Vmo63lmUlg/tUjN5IxfIKGAhTdJkTXdympyNp5RKZ/Ngue96aXGizOqx0lQwGcFGX27Im0EjJUyxbhcSZncCsep+l9JW8aDJTBZTicb7Wwvt+cBtedcHVbfuYdU8B35UI+KXJzLYDvsIRULG78WoxU5xMmG+W+CMgIgc1QIwYyc2VYjxTPLjYwHHjVUzpEnRJq140DdcmYDJF1U5JxPZiipohWx3EvUyhjcc2MpTF+9RlO/C8j13RsRKzYbD4NAMTYCEpzGGpYXv9yqsDGtG2//rFXbj0jgPBmGJG9SmAFccU611wn3716vvw37+6J/Cdw0CBVLxzpFxMZfm1Vrv7NGpiIu5TYXGyJmLJFNqEmWKM+9QcT6LEe+YkVI4XLi2XozOsxn16FRE9qmstWSPwC++2d1BZqDVJri0wl2TK4lKyPaPos70TJ6uYrbVw7b2HI8uemStkAL4rEQBO3TDq/V0p+nVBReEo2DyVLVdRriez+9Rlz5XkFRdIiektaTff5HAZQHKpNzNdAogQ2kSoT8vFQkfuU1kEFlDutEqxgIUM7tPphQbmai3c10G6S9N2sWe25rm0FVNMiCkaA24SkwNUTHE0HFOM6Luf/eXd+M71ewJMsVpKXrjaZIpWgWK9AEmQZ7VnFUzxout346Lf7g61zfXaNlyxsJziPl1u2h5T9Ce+7fvIY5F3oaRTV0z3qRtSn4afkUywC0bFnTimKKVSxEDmTHF1WI1RfCqAXxPRDlklg4hu7FbDBhVeRZuIficsKyzqiEJgpfksRlEqrhiuxhMnh7DcdHD3waXI2bq8dOMRMcXTNFMsWap6jbl0VKnoFyQaqxQzreq97LlPMwptjJjiiBGnSWJqC3UbI5UiJjRTTFw0uBVmitp96qlPg6kZLR2vKxay1z5V5/AnKWoljvR7JYzkjn3zmc4DAPceWoLtMh64aQwAUCpQ19SnSw3HY+t+IntUQrnr5T76McXklIxwTHElBcE9o7gKpqgqvQTvl+0YfTBlIV8AqLVcjymGQyT1loNLbt0PwGeK8i6UrAKGy2oFEq+ijYuQ+jTMFP1JXZzniZmxULexTk8S50LVqXKsDKsxis8GcDqAp0GtkPFc/f9RjcSUDCfIFJNm8uZAkmWgkIFaYopEwPETVf8YDreJduSlHItIyRD36cRQGZWiZZR5c73kfQAoFZMH3/D1zGdcVUOqiQC++7SijWJcjHWpYWO0YnnLWiXlaCoW5xusOPWpGMeG4T7tKKZouKTHqsVMMUWZQNy+byHTeQBg+4FFAMADNimGX7QKiSKtoIgj3X3q9yuVbhB21akSYmqpMeWaVtunuU9NNla0st9bE92IKTZst83dbBaHHykXvSIGcag1bc9TI25mSX/44U178fovbsPeuZofU9TvQslznxLEWeO4HIz7hgzZctPxqjHFCfeWmw4cl7FxTOkDcqPYHazYKDLzvQAm4S8bNam/O6rhM8WomCIHmGIWoQ2Qrf6pvNAS+xkp+25Er22h87W5T406oCdMKoM6IYsPewXBfYUjAG+5qTTUOmWKxoBkuk/jUg0cl1FrORitlDClrztJeduwnRj3qRhDLbgx8jPLFq04JUOuI4v6VAbf2/d2YBT3L4IIOH2jGEVKTOeZTxhwo9ozYoqdShbqoZXeWw7DZSky7t/baorQxnZ8N3maYjYO4pqfWWomGuAkNHTJNROmAnqkYnnLsSW1Q5hixQp6HnwVqGuoT9UzKFuEqmaKBYp2n4bFULsOL3sr1cSNJ7K/GEV5H7IsfpwjHis2ikT0NwC+AmCT/vdlIvqrbjVsUGEu5hlGm/o0wdgtN31Zu+0w/usXd+Hr2+6P3d5Tn2qV4HDZ8pKCvbaFBow49+nkcAnrRsr677K3pJQ6j69wBNJjV4KVxBStkNCmXFTupSjibC6EK2s9Hl7qwH0aqn0q7MlM3i/pGE5nRtFno6OVYiahjbjp7tjfCVNcwNapYS+mVSoUImNMgrlay2NoSUIb12UsNZ02o1gL9SXp9/M1OzAZqJZUnmLcIOyyoT4tZC+MEDi30ZYsbLHecvDpy3cEmLQk0psw453ZmKKvPpUQgxxT7pfjsuE+1UzRKuC1TzgFr3jcyW1CG5kQhp/RPQeXPG9OmJUK5Pg+U/TbP0g1ZtcaVuM+fR2AxzDzu5n53QAeC+D13WnW4KLRijeKahUDyiS0qbccj8G1HBffum4XvnvDntjtPfepHrxGK0XPjSgvTS0005WXRtIFZACfHCp7bGtyqBRgg6b6FFCunyyBe88d2XJT1Y6AFtqEUjLKxUKsqlIGrNFKEWPVEoiSY4oyQRGEa5/67tOg0KaYkhQvsB3FCMxzjFaKmWKKIujYeWip7ZnF4a4Di3jQcb44qlq2IgtJC+brLWwYVYNlYmy7JctG+cZ9qGyhFmIuYpjEfVo2jCJz/AQwEFO0VsYUG7brDfxZ4oq/uusg/u8Pb8e19x4OHCN8bjOmOFwpBrw3UVhuOp77NPyO17wUK98oiqEsWQU8/+zNeM6ZJ/h5ijolQ67LfEYN28Hu2RpOWa+MorDS8CRImOjG0aD7FMjFNqvBaowiQRUGFzj6u1WDiM4gouuNf/NE9JbQNucS0Zyxzbu7ce40eAXBk2KKKe5TZsZy00+qt10OLP4bBU99qt03wxULG/QL9bsnTem2BV/q+w8vY8NoxTNyMrufGC6hWrIwotlmpWgFhDZFgylWsrpPjdl8llxFh/3zmEYxLqbnGcVqEVaBMF5NLvUWZopSd9J3n/pJ+8zsFV7IGlMML00FqNhtJvdp08bUcAnMwJ0Z2KLtuLh7egkP0CIbQBU8SGI2c7UWNulc1KQ8xWVjgWFBtWS1GWs5Rq3lYLFhG4s3q+uPc6EG8hRXmJJRazoea8piFKUth4wCD1FM0Q0VkEh6dl/89U4sNx1sXadyd4uWym00hTaAMrRhe2ROnLw8RZexUGthU4RRvO/QMpj9uL+w0rChC7tPA0YxjyuuGKsxiv8N4DdEdB4RnQfgKgAXdKNRzHwHM5/NzGcDeCSAZQDfjtj0l7IdM/9zN86dBk9oE1PmrWwFV8qOQsN24bKfVN9yVKX8pBQDWale1KfD5SI2Tw7hK3/+GLz68acA8HPvBNtD7EJmt8IwX/fEU/Hcs05ApRRkimbqRmb3qTGIZnGhSoktwGeyZV0GK2rCsRAavKeGS8nq05DQBlADuD+ImTNz14ulZh24o41iMdO1LzUcPEJPZO7IILa5d2YZTcfFAzf5z3I4xd03X7O9ATepDNuiwcAFQxFFvs0J16GlZoApAvFiHsXGJLdyhe5T28GpmjVlcZ9KmsQhozh73W5fZ1AKggPqfi7H3M8rdxzEed+9BU//nU146aNP8r43341aq50pmtsJAu7TegsbdXlA8/5JIY5TtFGMS8kIu0/NOHK+puLKsRqhzYcAvAbADIDDAF7DzB/uVsMM/B6AHYMi4pHBNGrgFLaRVO1CHUO9AFIT0XYUU0xSU4bVp/L/Ex6wwYtLmIMfM+Ou/Qt40HE+u1AFn8mLyf3tM87A0x58HMqWEVN0g0wxq/p0OcAUsxnFghHPAZR714oRYyyFBu+J4XLi8lGNVlBoA6gZezhPUf5W7lOCVShkyqXz17szUzKKut5l/P7MqvLIg08Yg1WgTLmK2/cr5ekDjQnOSNlKdPfN1cwBN36AXJIVMtrcp6GYonEMx+VATDH8u4lwnuJKy7xNDJdwwkQVO6YXU7eX906Yori6w+9soA9WilhqOpGT3V9uP4gCEf7jJb/rVUgCgv1JJoWOy22eBnOSWTAWGZ6v2Z7r02SKOw8poygTAVVQPYEpRrlPc6a4YqxGaPMBZr6Omf+DmT/KzL8log90s3EaLwZwYcxvjyOiG4joR0T00Jh2voGIthHRtunp6VU3JokpZnWfymA27rlPFVNcaNixMzzPfaqNQlgYAQTZ2u7ZGpaajifhF2ydGm77rmIkYJu1TwFVGi7LC1YPMMUM7lNjsOzIfaq3HU9JfwhXtAF0kYJWcGYPqAFJGHLRimaqYfi1KYMxRWYkqhhrLQfMytW6YbScqYD49IJiR2YKzkglnik2bRe1lmPEq+LbYy4wLBiKcJ+GmaNvFLX7NOYctuvC8krTde4+ldXkqyULDz1xAjftnkvdxzOKi03dNt8LYiIotNHvUATjFYGNWU0KUMZOjinncLjdKJp5v8IUlxsOmo6L9aNlELUzxXUjZS8fl0jpFMJGUeLXGyLcp1nW0cwRjdW4T38/4rtnr+J4bSCiMoA/BPD1iJ+vA3AyM58F4D8RU3aOmT/NzOcw8zkbN25cdZs89ekqyrx5RtET2rA3cMXFFUVJ5zPFYAwICA5cwi5MpggAP/k/T8afP/G0wHemW7HlhoU2lE1o03I8BpqFKbpsCG2qIaMY5T6tBwdvlR8X365wRRtAMVGv9qmxb10vBluylGS+M/dpMHkfSC71JsxspGxh41gFBxba3YF//Ikr8eWrfMeIFKoeM1YTSUo2FwaxfqSMAiUPkEsxMcWwEQzfa88oFtsnZCaCTDF7YQSBv/hzAWdunsA9B5dS47YNjyk2dNv9eLnJ4qMU0FETjbqRimHCLM5vMsVk96n6X0IlKqZfCDyjew4u4ZT1w8FzRcT2lxo2ShZ5k2vTGOdMceXo2CgS0ZuJ6CYAD9aVbOTfPQBu6nL7ng3gOmbeH/6BmeeZeVH//UMAJSLa0OXztyEpT1EUjGnqU3lJZRCttxzI4eZicu/CZd6GjZUlxCias1wRcJgxRQBe2oEJ5QYyK7v4v8tsOC3vabnp4DjtrsscUzRyxKQdcXmC4cG7YrQ5CuF0CTl+eJFh2bbpuCh1UBDcXNpHkKUouFQHGi4XsWms2rYo8VLDxrX3HsZ1hnJysdGCpRf0FYyUi4GcOBMyKRERVVJu32KEUYxMyQh9DscU484RrGhDkeURo3DNzhm8+zs3e8etFi2cuWUczMAtKWyxEWKKpsEx75ftukYBCXUdUSxfrY4RYxSFKRqq9CT3qcRXZfI77gndDPfpwWUvnmieK8x0pWZt2CMC5DHF1WAlTPF/oJL1vwM/cf95AB7JzC/rYtsA4CWIcZ0S0fFEqkcT0aOhruVQl8/fhqiUDNdValJv6ahickUb331aDHwGEBsnsx21/qDEsIJMUYpd++fbfmARG8cqbQn+URC3ImvXj1nRpmwVwBwdQzVRbzk4Trv3shQFNwfLStFC2SqgkpAnKIOVDF7VkpUYK2vaTmb3ab3leCxf3LdpkwBvZXSzoo02LEn1T00jtHG0ggPzQaMoyyMdMNyqSw0HI2ULRP5kxR/E2881ZxrFUiGFKQbvKxAdUwwfw69oI+7TjOrTjDHFn99+AF/89b3etQyVLTxs8wQApLpQwzHFQPUox2SK8Fy78j5FMcVa0/GMv4lIoU0aU9TPUK5rtGIFJnjLTRv75utePNE8G0MJQwAAIABJREFURniSLUt+mX3Quwe5UVwxOjaKzDzHzDsBNAHMMfO9WgTDRNQV9SkAENEwlIv2W8Z3byKiN+mPfwLgZiK6AcB/AHgx96iMg+syDi02UGs6fuzAGLg/cdkOPO3fL1MJ+QZTjBuMhC2I0MZ0B8WtE6gEML6IJzKmGHCfLrSxxDhUihYajl8b0nQ7phl4Qa3lYGpY5TxmSckw5fCAciWrqh/RRnGhbivDaaQCpDPFdqNozuzl9PWWWqC1Uip4A3gaW4x2nwpTjL/+ZcO4bxqv4NBSM3Cu3br4uhlrXKj76TsCiW9FFbGeNyoZVYvJkwfpi21MMSWm2BlTNFf2yF5XFvDvQ7VUwKaxKo4fr6YbRUeYYtB9CgRLKpol6JLcp7WWn7RvwnRpeu7TiJii+T7JfFPe+aGSMmpyvTJJkmo25jHCgjdZ8iuoFtfpG7n7dMVYTUzx4cw8Kx+Y+TCA3119k7zjLTPzemaeM777JDN/Uv/9MWZ+KDOfxcyPZeYru3XuMG7bN49H/stPcdmdB7zOZs4Gdx1exr75ulc/M7v7VAY2/0WMSzOQF1gG4ZEI96kpAtp+YNErHp0GiY3IYFIsBN2nQPpyNDWd2Dw+lC0twUzJAIAP/enZeP2TTostCG6u+QckxxSZOdIolg2mWG+53qRkdrmJpuNiYqjkMYc0sU2S+zQ5pui7TzeOVeC4jBljIrTLY4r1wD7mtQOdMsUM7tNQjLphuwExWVhI064+TY8pqkWGsw3W0mZhzBK7fNjmdLGNPOPZWgu24wYmpyZTtY0CEmIUoxS9cTHFUoSa2Xbb8xTNcIQntNHPrVoqBNyn5rMLH6MtpthU5fmKlj+Zk/hibhRXjtUYxQIRTckHIloHoJiw/ZrF+hGppuEPVOZs0HyRysWCl/qQ7j5VHdiMY8zG5Cq2HNZG0c+rEoRjiktNG8tNB5tDs804yDHFOBetdqbYiFgix4S4mMarpQ5SMvzPT37QRpy6YSS2IPhSw/aMjrQ5biCOYrxqHysQU5R8TRl4x6slYxWDFKMo6tNAQXBh/vHXL+7K0UrRyyM0DeAuvaDz4eWWN7AthmqTAsnuPnMdTXWfkpiiKjxtsnZhRaYhlGOEV26Ict2bsB03EFPMyhTrHnOq6/OoNonYJilHU/IUmdV9NPuJaZSj1KdRIp5ajFGsrMJ9Kv1gqBx0n3qlGUNGsWyIxASqQL4fYzf3C2/7hi9uwzsv6rbk4+jEaoziBwFcSUTvJaL3ArgSwL92p1mDhakR1dHMahp2nFEMrIwePVDIiydqTfMFj4spOtp9euLkEDaMVnCGoSq19JprdYMFAQgIM5IgL5S0qxwQ2kiNxxSmqMUIY0OlDlIy2tsXt3TTYsNuYzO2274yCGCyuPbkfXNmL/FWMYoTQyVvAE8bvKPcpzJAJblPlzyhjeWlTJiu0t3G2pUHtftv0Rj8BCK0WopwnwrbGA9VK4pCrdk+4Eel+IhhEUNurpIBxKdkBJhiB2XepM37PfepOs/mqSEw+/cmCiZLOrTUCBjsZsgoejFFjynGxBTjhDZ20ChGCW1MoygiN+kHQyUrMHGR0EOYKZajmGLDFwDJ/RGdQnjbHdOLibWCc/hYTfL+FwH8MYD9AA4AeCEzf6lbDRskVIoWxipF7JnzByxzNrjctPHg48fwgE2jXgpEVGBcIO4yqU1pGsXYlAztPl03Usa2dz4dZ26ZCPxeNaqQyP+ViNlt9PUVdDvUfuEEZSB6gVNBy3Fhu4yhkoXxajEbU2S0qWABldwcxRTDhiG8aLAJue9Ryfv+eoquN/BIHuD4UMmPKaYM3pHu0wxG0SyrtkmrdU1Rze7ZmrdorBjLpQijOJowiM/XWygX1cK21RShTcN22iYPUTFqEZhJn+0opmh1npJRD8XYZII3JcXgk5YNM653ZrEZYoqhlAwKCm0WIyYZdWMdRRPKG6TrnBq1T03Xu1WgAAv3maK4Ty3trg4zxVBOZIT61PQgSD8ci3GfmgY0RzJWwxQBYC+AqwHcAGADET159U0aTEyNlLE7wX26cayCn/7tU/CMhx4PQBvFmIF1dllVx5cBJYv71HY4EOsLw8wtkxcsSjEXBRkUhSkG8hRTStYBPlOuliyMD2V1n7qwIi4nTowRdp965cUiBvxGjFE0Yze1pp9XKQOviinqlUsyMsVAXcsCYaRspTBFX2gTxxTFCyDfRzJFbxCPdp+KwQ/L/aOuI6xeFFYUTlupFAvePfPzFLPXPi3FFHuPbpfEFIPu0ym9ukucIE3aKobo4FIzGFN0zb/9tgnzjir1Fuc+lUmWq2sXyzHNSV0p1MmlXTIBrZasgAdjznB9m4iaZC81Dfepbp/oFKJcrWEXfI5orKaizZ8DuBzATwC8R/9/XneaNXhYN1LGXsN9ar7bUS6oSsTMTnB4uYmp4bL3Qgbdp9Evu+nqicJQ2TKYonafRuQvRaHsMUUxisGlo4D0FT+kDRNDpYxCGz9ny0SBotWn4biaX4g6Sn0ZPbBIegIzo2473kohfkyxmF19GlHRBpCi4EkxRRtF7e6uliyMVYue8WvYDg4sNHD21slAu6JiiqMJwpC5Wstzo0m86ra987jszvaKTvWW44lYBL77NFjgQGLGgN9nROQRK7Rhn41ZBcpUQg/wJx1yb6RN8syS6gQ3bRfHjysWfmixEWhbUwvGxHBJH5R1UBcjmPdy045Un5Z0lRnTdRx2n5oTTCDOfeorhOdrKic1zOqimOJyw2ljivJ8zG2ltGB4YpUjGqthin8D4FEA7mXmp0IpT1dfR21AsX6kHHBzmTNOc0kZQZLQ5vByC1MjZc9NKexh/Ug51n3achmlCCMiqBb93DIv4blT92mznSl67tMEpiiuo+GyCG2S638Csshw+/fFmIo2iw0boxHioigWJO5piQV712IpA6EWYfVFCQcM96ll1KZMQlzccrRaxP75Bj548R2BuNfnf3UPXnXB1Trh2s853DhW8QZ+EXKdpY3i9EJDDWgN22MAAj+mGMUUbZ8p6kWA//Pn2/Gui26OuI52phjlPlWl1goGA/X3iVMCuy6DOWh4WlmZYiukPvWMYgb3qeNiw1gFBVIJ/AH3qeszOgCBPjhaKbaluLiuX2YuDGGKZuxVFQT370940mS6TwukxglTITxfVyzfzEkF1L0z+7qoxUUg5Att2mOK9ZZagGC4krtPs2A1RrHOzHUAIKIKM98O4IzuNGvwIIvyAmpwNSe8y00HQ6G6iEkxxdnlJqaGSx4jk4HtuPFqrFF0XDcQmwijWvYHJr80VkajWAoKbaJSMpKYogyeQyWVktEMyeCjYMdcT1zy/FLDCZUii2eKIigQ1bBAmGJdM6DxahFEwEFd+WS86gtt0mOKLoja3WOjlSIuu3Ma//nzu3DxLX4hpuvvn8Xl26cxs9wKzNg3GaXeRGRzyvoRTA2XcGChjpqudhRmirKEWJzQZjzgPnWwf74RGX8ML7EFAENl9TlgFG3NFIf8knyCaqkQKbQRw1O0DKaYNU9R9x+Z4MjzHq+WUKD4MIPsWy0WsG5E5YGafVFigE6IKQJqUheeZMi+kepTnfdq3ifHVYZRnnE7U1T/LzWUd4mIAu7T+ZrtsXwT5ZBwr73CkwhtSoF2A9H1bXPEYzVGcRcRTULVHL2EiL4DIH6V3DWOdaO+URwqW4G4Qa1pRzDFePfpzJK4T4Nuy+PGK7FuIbXOYRJTLLQxxajyT1Eo60VMJb+uZOwnL3WS+jQQU6xmq3/qcrT7VIySOXaq1eHDKRlJTFGxizBTrBQtr1g24MvhHS0SkkWOAaTGviTGFp7Rm4xOVjsAoFfPAG7bO+8VdQeAjWNVjynunlXpGFumhjwGuViPHtCKVgHVUiFWaCOMrqoTw6cXGpH1SRt2e7UWL8UnpD6tFq1ArFIQV0rONzx+Ska4/qiJX911EOf/6DbvfFFtKhQIE0OlRPep1L3dMFrW7lPTKOriG7oN5gRwNKKerD/ha++r8o7XA0ZRld4TVhY2isIUay3/vofzFMPpGECwpBzQXp5PJrajEepTMzc2RzpWoz79I2aeZebzALwLwOcAvKBbDRs0rDeY4kjZ8l54ZsZyRG1E1YnjhTaTJlNs+kyxYbuxA0yS0GaobHkuJz8lozOm6MUUzTJvGdyn9QBT1EYxJa6olH/t3/spEYZ7Wq8sMWq4fyoJTHFGM8WpUIk7mSRIbdJq0fJn2JoBWRljis2I2qoA8HsP3oQXP2orHrhpFHdP+0ZRxDc7phcDq9wrpqiMogh+jhuvYtNYFQcWGpHrHQpGysVIoY2KKfrGq95yML3QQN1ur2Fbj2KKEYpScZ+KOGisGmTtUXmK8gzN5H0gOOExcdFvd+O/r9gJIDjZIQpO8KZSlg2TZ7NupIyZpWaAxYr6VDwBpgJaMcVgfzInUGFIRRsz9ipMUdSscUIbAIZRLHjv7ny91RYLl+OYBTTC7E/64lDJagvd+H0od59mQcdTByI6iZnvM79j5su616TBxDrDFTdcKXqDphJutL80ZasQmcbQtF0sNmxMDZdBpOTaEsfYpMUBh5ebOGEimHjfctzAOodhRMcUO81TVPsFhTbkXWcc/JhiEeNV9fdcSqk3cy07E1FGKWolhySmeHi5ifFqsW2WLtcpCj9JWZir+XlhRc8oq/N/74Y9OGGiinNOWRc4ViOitioAvPoJpwIA3vilbbjrgL/2nwxMHHKFTg2XsNx00LAdLDRsVEtK8LFxrBJYESLKKA5X2tdUZOaQ+rSABc1SAcX4y8ZSRpEpGRHLKMl2f3DmiThxYgjHjfvLWI1Wo8VVbUzRYOFWoX2A3jtXR9NxYYfYV5iRT42UE9WnUve2XCxh+/7FgMFueTHF9upNI5X2SYb07XB4BDBiiiZT1GXeZJIcJ7RRx9RGsVTwykfO11o4caK96EZYaOPXrA0yxWrJaltRwy8tmDPFLFgJU/SWaCKib3axLQONAFOsFD33i3S44RArU4vztg/YszURgajjFQvkvYiimNs/356YnIUpesn7HaZkhNWngTxFK50pLnuz6YLPFFPcp3HXE6X+jDIMSTHFQ0vNQAxYEDaKQ+WCkfSs2i0uXTn/+T+8De/9/q1tx2q02gUqJk7dMIr7Zpa945iDrenGMpebWqjbGNXLQ20KuU+jBrSRcvuaiosNGy77zLdSLMAkh1GFvmOFNs0gU6xog/2Y09YHtj9hvIp9c3660v0zy3jhx3/lFTc3C4IDwH/+7C68+cvXtl2P5AEvt5zAZCccz5saLnmVai644h68+NO/xoVX+/N0Wdd0YqiM2VorwBRloirvr8ncou6n6QUJo2SpvEvT5epq9WlRq1nDaUFmaUPpw5Wi8jzZjov5ut2WoyjninKJhoU2MqmKcrXm7tNsWIlRNEey07rVkEHHupD7VGKK5lJAJkSuHYYIaURFZ6rKHn/6ehQLhO/d0B6aNVeViEK1ZMYUJSWjwzzFplS06dB9asQUJ7K6Tzn6eqKYYlRcTdocLbRpepMOE3It8wH3abA8lly6nH+u1sINu+aw1yjcAEQvTWXi1A3DaDnsiWfMeqhmHVOziPhC3U+l2DhWQdNxvVqoYfWpOk57DEyqCfnq02Abo9ZJDF9H1FJk9Vb0ShGAWvzYNIpf33Y/rrtvFrfsmQfgTzTk2V654yCu2Xk4cAxmxl6tvq01g0YxfN7J4TJml5u46Le78c/fvxW/uWcGP7vtgPd7o6VWPJkcLmEuXObNDQptwkwx1n0aoz4FgsUabF3mzSJC1VgcQBAop1cKrjTSsN2A6zt8rmYGoU2lZMUa0Fxokw0rMYoc8/dRDdMoDpctb6bpu1fa3adRik0vXUDHu0yX6OapITzzYcfjG9fuahNF2I7b5ooxYSbv+xVtOnOf+kyxszxFGTiU+zSj0MYoxmwiq/vUHEjCmFlqYl3EklkycMzpiUlFVxMBfCMiA7iUkJN0mUtu3Y+55ZY3CYpznwpO3aBWKLn7oHKhmstJmdchTHGhbqu0E238tkwpF9rtexfa9jGPEx7E5dr8mGKwjeF+FXUdyl3ZnrwfZxRPmKhioaGMOjPju3pSJxMBP3lfnWffXL2Nkc3VWl4/Wqi3As8/fF7FFJu4c/8ihkoWHnPquoAatekoVjs5VELTcXF4qelVCZLJnR0TUwwLl/z3u/1Zi8Ez+7owRatAGCpb7e7TAFP0hTaAmqw1bTdaaKMn2RITDntPzEWfwwZ0OWJ5sBzxWIlRPIuI5oloAcDD9d/zRLRARPPdbuCgIGgUi95LtWTk6JkoxSTvy8srlUFEfFAgNXi8/DEnY67WwvduDLJFJ5UpGhVtWk6bOCEJMuMVFaR5LaUs7tOmP5sWRpNW/9SOcZ9GMsWIma4XU4wU2iS7T2UlkqGS5bFpYWim+9ZkAF+56j489YOX4p061y9qFQ4Tp+pFYnceXFK5kbbr3RtTaDNmLEyslohSn09ap/a/de9c27ULRiIGcWHBcUwxyn0aNjpE1LZ8lFKfRl/v8RPi9q/j5t3z2HlIqWjF8Mkzlf/3LzRQazmBurVmsX0RSsn6lFFMsd5yccueOZy2cQTrRsoBNaqsjSnv2L75hnf/5L11I9SnZghCUGv5XpAwZGJmrnLiMHslDKslK6DkNu+BecxK6P2LM4rM/nsRjhNWDdZpljMEoldCyRGPjo0iM1vMPM7MY8xc1H/L5/FeNHIQMKzl+5ZeqcLloPs0UmgToT49vBxURoqQpazFBI89bR1OXj+Mn9y8L7CfWiUj/nENlSy0HMVu6jHpAnGQl/L2fQsYrRSxdWrYv46ilHmLdwrIwFHR9TYrxUI2phhlFKm9IHeUUCCOKTIzZpajjaLnPq0Zy/bo4/hM0ReDSOzxxIkq7ti/gJmlpjcANiLcjiY2jJYxWinqFR1U+x+u69UGY4r+JGKx7lcdOWm9ega3ahdkpNCmHMEUjWLgQARTNIyi67JWarb3q6GSld19qmPhe+fqgcncYsjzIH1dBnaz7fvmffe0GDhRuoYFY/Lu3LBrFqdvHNXuVL+/NW0/pggoYy0G1mOKbntMsVpU+ZamQjcppihio50HfZWxo8u8WaSeWTiVw+zyckzpg9NGZaUwSt57qNrvp2SEY4pRQpv4uHSOdqwmT3EgQEQ7iegmIrqeiLb18DxYP1JGtVgIJCHLbDo8C4ta/wzwX/iw+1QGWCLCcePVtlhRmtDGXAG9kTCARUHO7biMh20eDxirLMn7asAsePuNZyj1Zpb/MpHEFE2GFVeIermpWFlUTNFznxrqUz8lI6g+dVz2ruHN556OPztnK07fOOIpGRu20yaiMEFEOHXDCO4+uOS5Ec/crCrVmG6scc992sJCveW5U0crRWwYLWO+bsMqUKSSeLRiYalp4x++fRPe8tXfespTAEaeYiimaLA/GWCj3OzVkFFUjDL6ekUpvXeujsvumMbDNqu58WIbUwzuv2CUwzOZoihLN4hRDE0+1un803rLxQM2jWJquITZmnLdOi7Ddhllo07rzFLTc0u3jFgxEIzVVkoWmIMTrbjwCKDCHYBKsxE4hvv0/Beeib97ZrCeCRF5htEU2gBGucEYpgj465qa5QJV2w2j2Ca0cVC22kU/OaJxtEwdnsrMB3t9knWjZTRsF4UCGUwx2n0aVasQUEKbaqngvWSSE2h22LJVaHNztVw3ufapYSSics+SYKZgPHzLZORvaWXeTPYzXi16g04c4oRDRavdKEbNdIsFNbiEmaIwuUj3aUnUp1IlxQpUSgH8GJPtsscoH3zCOF7xuFPw8s/+xogppt/jk9cP46bdc97gf+bmCbzk0VvxlAdt8rYxV9ZYCBX+PmndMA4uNjFilIUzMVwpYrFu45vX7kLDdvGQE8e9mFU4pihrGQZXvogXZJm1dJkZ9VZ76oZg07gyXjsPLuGu6UW85vGn4Obd820VksKTOlORawqZxJsSxxQnjXjx6RtHsWe2ptzdDdt7nypFyzOKAAz3aXD1DVmpBPDfoYZR1i1JaCPrle6YXgIRPPemyypeHn6XBFaB4DrsM0X9jMzC9GEE1zUteQW+pV/4S3kVIqvf5CXesiOfOnSAdSMVVEtWYLHUuJlknPpUqtkIfKZoVpFpT+dwXEYpgSlK7KjWdLySXFkhpaYA38Xnt6+AAqXHFM1BQ62UkRxTdGOMogzqZu1Rb0FW4xxEFIijCjyjGCG08YURwWLM0mbAH7hdl9tWLKgYM3CVypB8jzfpajXCFCeHSzj/hQ/HGcf7a2GOeu7TFhYbwRJfJ69XccWxCDUioJizrVdoOHXDCD7w4zvwm3tmQOQzIHmuwmrCpduAaKZoxhRbjqrnGccUqyUL60fKuPSOaTgu45Enq7XH/ZiiNsyhSZ2pyN07W/fEMBJ336iXqQq/W+b7c/qmEc/4zS75CzOXiwVMDvnbjYYKZctyYbI+pFyHeV+AYLWmMCaGShguW1hs2Lpkm146KiX+L308LLSZXtQ1eCOedyVUWWqx4QQmUI88eQpPeuAGrB+ptMUUl5p2Hk/sAEeDUWQAFxPRtUT0hvCPRPQGItpGRNump1dXr/ysLRM44/ixwEoOcSkZ4dmaYHa5GZjpFiOYYlTdVNvhyLJoAm+WazuRKx+kQc5/VsTsNqmOK6AGEdOoZ1kpIy4loxjKEwTgFdEOxyDNmpGCmeVgHmhgez2oiwtbxUAlJSNY0cY23KdmvU/PfdpKVp8CwIaxMpabDqZ1YfCouGDJKmCoZGH/fF1V7TGM4tZ1Kq4YpxoU5ly2Crjw9Y/FcMnCJbfux2il6N0rGXBP0scyhST+Sh8RTNFwn2bJez1+oopb96r455lbJtSKEylM0VTk7pmrebFsmdhsjHGfSjoTkdSJ9VfOUEwKAfcpACOmqPrVgYUGCgSsHzWNonqeYYFRnGiNiHCiZotDJQuWHhccjlZWC6SPeUYxHFOMylPUBRfkPVxu2oF+cfbWSXzpdY/xciPDKRm58jQ7VmwUiahCRC8lon8gonfLv242LiOewP+vvTMPkuSu7vznZZ3d0zPdc/Tch26hc0bSaGQhcQgQCAEWeIUtgcHmks3hwF7jBR+wgGO9Zn3thgFjBcZ4bQyEDUYmQkDgNeaSJZCFQCdYaHSMNNLcR1/VXVVv/8j8ZWVlZ9bVVd1VPe8T0THTdf6yKyvf7733fe+pXgq8HHhnfKajqt6qqrtVdff4+PiC3ug3X3oun/rly/3whzOKc8nh0zRDcnRqLvxS+4+rv3jB/Jok8IUf8ZZRUWr9KqthS652KGQzrB7OhaUAUfIpXq/DL2Svz5M1Etq46QnJJRn+v9FhsJOx8KwjyVN0+ai1DXKK/3lggnM3rAwVgm7NUG+U4/m5qBFuJXzqBvI+HqgxRxIEFOB7dS6nFvUKdwSGLK2+zO3+d5+2mo2jRd743B1164XaBXdHINyJd6mBZA+wmM8w7ZpBtDC0elOgQB0dyrFlbIihXK1lWq33af37TNaFT2c4a71fxhIPn8bf120qt60eppjLhD1uj03P1Qx9pib6AuapTw+cKLF2pJBYNxj1FN1YuDTRmjOKxZy/aXNCmyQRmcPl0p0HXIznFJPqFIP+xHMRoU2acCY+USPeTN9ozEI8xduAG4AyMBn5WVRU9eng3wPAPwF7ev2emch4o+nZ5J1kPutR1XqPB2qzFB2ue0w8p5gUPm0Ukol+oWfmKk1De3GKOY+Lto4lfvnT8qOOeK3bqqFsaknGB//5Ae7aewSY7zlALdRW1fqcYlLfxkLWmyehr42NSlefAvzsrs3ha0CS+tQPn2Y9ieR+MuFGJ633aZR1QRP5vYE6cWXKhck3in5OLWoAnSFLu6C52593tr/Ze/NVp1PMeXUX1WLcU4x1qXHHFWco54WPreUe0y8Xrizjwi2rgtC2N19okxI+VVX2H5/hjKCMJa4+jefz8lmPkUI2NKJOZXpsanaeeMh5i0P5TF0a4MDJmbrQKSQ3Qk8bMOzYMlYMXz/r1TzFBvvX0GC6v6fblOw9NBkquOO4DbE7/yZL6SHReYX+NkuxLRbyl9qqqtd1bSUdICIrAE9VTwb/fynw4V6/b1R9OjVbYThhJxmt73N9HqtV5cCJEs87Kxo+TcopenXNf/3X0SbF+7XQz0y5mpisb8R/f9UFobQ+TrPwadxrcp6iqtb9XWbmKnz6jsfC3FHSbjreexRc+DTZUyyV5+cUs54kytqja/zZnZvD14Ak9WnVb84cmW0XnXuX1B4tjvMUnWQ/3VPMhX1So2pIV5aR1M0G/FrIoVyGa8/fAPihwA++6oK6jhrb1gyxc+toYDgfTvQUm5VklFoInzoF6gWbR8Pnx42iE8GMBD1G3f3Hp/1c4KaxIfJZL/T2x0cK5DKS+He78bKtXLLdD/WHMxYnZ2s5xeC7MjaU59kTJQo5r26e44GTpVSjGN1oTc9VEpWnDtenNAyfqlKtJp/bDneOudc9c3yEa8/fwNcffDbcCMSplUY5o1hJfWwhQWgTP1YjnYUYxTtE5CJVva9rq2mfDcA/BRetLPD3qvrVXr9pRuqNYlKz4HBnV6kp2R49NMFEqcwFW0Yjj3NquYhRzMq8cGUrxfvgG57SXIVim18Cd2FNwu/jml6nGO/CsWooR7mqTM3Wh21cjs4VxScKbSJGyeGHf1rzFI9O+S3ekjxe9zfevWN1mK+77sKN/vqL8SkZ9cN6wQ0prvr1fZXWw6ePHZ7Ck2QFI/hGzxmIqAEcHykwnM+k7vLP37yKBz70sroL8E17tsdeO8dt77o6XH+8zAKSjd1QPjO/bWCjnGKwobpg86rwsa6HrzMC7m+7Y+0wDzx9IjwPwmMvZBnOZ8K8cDGX4W/evIez19eESY4P/uwF4f/dZ3R0ql5oAzAaGMxi1u8uU47kFC/cXC8qq5Vj2sInAAAgAElEQVQ11ecUG3mK0ZyiF6RVqinlRo7QU4yMw/rLX7yMv/jmTxOHRsP80qjJ2fTw6fycooVP22Ehf6mrgV8Wkb1ACb8nqqrqxV1ZWQuo6qPAzsV6P4dfkuGHfZJmKUJkZxc5OX/wxDEALtlWE7MkqU/zmcy8CRvlarVJnWJNQt6o0LoTXE7x+48doVxRrjyzviF0kqcIvhGsM4pBji7e/itKNmKUHFOz5RThzHxPcf/x+WExx3A+y86to7zl6tPD287ZsJL3RGrJMhGj7PehjNax+UYxDNE1CZ+6spBDE6VgoHHy5xcNd0ZziiLC/7rxYs4IWsYl0cgjiVPMefNEJJDsKRZzmTB82sp8zstPW8PObWPhuRGtcww9xeBc37CqyGORCSBTEQX3cC7D/hMz4Xqfe+a6pseVzXhhGVApZhTHIvWa2UDVXa5UOTRRYsOqFE9xNpZTbOQpupxiPkMmUKU328BmYupT8D/Hd15zVupz4j2IJ0vpIdF5vU8tfNoWC/lLvbxrqxgw3EldVScCSTCKCe3R7n3yGCsLWc4cr13kspG6KkeSp1iuaMPRUe6LW5qrtiQCaQf3Jfujr/2YuUqVf3rHVXX3J+UUwfe0NkU2426clLsYJglt3G3ReYoTpTJbI112HIWsV9eKDfwJDUmeBfgXZ+c1pREN37rwae39/GkGbjff7G9cDNre+e3b0sPZUe8wHip95cWbG75HO0RrD6HmKaaWZLThKW5fO8xt76ydF1HxTjbWEHzdSJ6RYjbcHNU6FmUYymfCqR7NNh1Rxob9Vm/OGLjnupxiIRuETyvK4clZVGE8li5IFNo02WBuCT1Fv6lHVYM6xUZG0YVP29i4RnsQq2pDoU2S+tQmZLROx1dOVX0cGANeFfyMBbcte0KFZLWaupMMc4qR3OC9Tx7j4m2jdV+YaJs3R1xoo6pBsXv6x+VGV03OlrvvKQZCm+NTc8nT22Mtz9ImZThP0Q35bVS8X63zFJM3Hn5OsfbAalXZd3SabWvmK2hbJdpR50RsYkF8KkIrDdddrV2jnXrUEPZyRx9v3RbmClNKMvwayErb8znd8x01T9F//rqRQphXhEirxFy27uLdakN7qI2Tmo2UZEBNqVrMZch5QrlSjRTupwltojnFakPjtWG0gEisJKPaLHxa/36tEPUUD03MMlfRVA2A/331rzuz5SpzFbUBw22wkJKMdwOfAdYHP38nIr/WrYX1M6FCsup/oZMu2PFehdOzFR5+5iS7tsU7xiTkFDO+ctV133D5y0bF+74gxBcbdFKS0QhnpI9Pz80rgYD5opO0SRlhTrGUnlOM9h51TKbsiou5TF1D8IMTJUrlaqi07ATn1ZQryvHpcsxT9OqOoxVPZm2gQE0T2UAtZCrS26bNxXlNvtM9xR2BEvSRAxMNc4+N3svhNjrusx1fWW8UpyNdoaIbzHZqbceG8xyfmi+0qbW788gG5/GBhMJ99xiobx04M9s4p1jIZjhj3Qo2jBYDAV7z/H9YktGBp1gqV3nyqF/ik7b5i07USJowYzRmIX+ptwBXqOokgIh8BPh34M+7sbB+Jpy5p76YZM2K+TmsfKw92v1PH6dSVXZtW133uGySpxjuCpVsJtK8uEH4NOMJq4fzHJ6cbbujTTNc+PT49ByacMrMD58me4rHp5sLbdwFw20EVJXJ2XShTdRTfPKIf7HYugCj6HbxlTB8On8yR+gpthCiXteGpziSz7aVI2yXqHgGahNGkoy7y3vf88SxMArRjpFK8hTXrMiT8YQzx0f88Gkspzicz4QbTBEa1uXGWT2cY++hyfk5xeFYTrGqYT3g+pinldTRppn6FOCLb7+KQs7j9vv2U6lWqabU4DpqQpvWN66FyDUhPM8TUgpQr1S1CRntsxB3QoCo21ChfgDxssWLXLin55JDe/E5hA+5bh9b6hVvtV6N3vznxjr65xqET8EvWH/muN8ZpatGMesxWaowPVdJDp8GUwnCdYzkyWc9PvaNn3L/U8fD2+eFT1toCF4qV6lUNaUkw6vb1T8RXCy64Sm6xuJJ4VN3HK00WA6NYgueYlrpRbcYyqXkFBOOY+vqIdaNFPjBE0cjHW3auIhHPUWvJrC563dezPPOXud7ijOx8GnEKBaz6QXzSbicYvyYXKs31xM0Gj4dH6nfzIZzJGdbzymCr3D12z96VNR5iumPTxLaNCNa4rUvGFyd1GgD6pWqSRNmjMYsxCj+NXCXiHxQRD4E3AV8qjvL6m+iF+6pFC8mrhZzO7Zo6ylI8RRjXmalMn/MTRJrVuR5KigC76bQJp/xOBS0KouXQKjqvEL2VcUcn3zjbk7OzPHGT30v7P7jPEX3Gomjo2JGMfxSJ2w8Ctn6i/yTR/xjd+KHTnDvf2RyfnPm+eHT1o1iWuE+RDzFRTGKkTZvDYyiiHDJ9jF+8MQx7nr0CCuL2br2hK28lyN63q4bKSAijBRyCZ5ilqFc0LO1zfD/2HCOkzPlcNPmjsk1UBgp5MhmhHJFOXByhtXDuXmbGhGhmM0wE5uS0WqY05NgyHCrQpsmHmiUqJr9ySNTrBvJp4pnopEm9ze2huCtsxChzZ8CbwIOBz+/pKp/1q2F9TPR8UL+lya5pyXUvL2ZlM43tZxiZv5zy85T9P9tpD4F30NznVG6K7QRDgcF1bOVal2Xnlp5Qv1xPf+ccd7z0nM5MjnLo0HxenxyRsOSDK2NyIHknW4xVx8+feLIFBtWFRZ07O79j7gJ9glGsRY+bSOn2IJRbKRQ7QbxcVAzcxWynoRdleJcsn2MvYcm+cr9+7nxsq1tjR5KUp9G8VW5/t84KXza7qbOdYly/UPdWvecvoZb33AZu3esJuv5ubZDE6XUwvdo2YqqBuHT1tbimnpUmwltOvIUaxvlJ49OpYZO/cfWrh/u+2MlGa3T9l9KRL6jqleLyEn8ZtwSuU+X86Bhh9sFVlXThTaxrvbTQZPueEjIXYTjDcH959aHTxsNGQbfU3Tt1bqdU4wawpm5WjFwI2/Djc6576ljnLV+ZN7kjFY8xckGA1ILWV8hWa5UyWY8njw6taDQqVuTEyxB/cDXMHzahvq0lfDpqsgMxV4SF9o0K925JMh/z1WU11+xo633SvMUHU5o49f6VvCCDWMYPm3z/HURmP3HfRGN+6xEhJdesNG/LSjePzlTTuwv6t53JlKKkha6T8KToE4xpdm9I2wI3obhj84CffLINDu3JY+lglp048DJmTCqYTnF1mnbU1TVq4N/V6rqquBf97PsDSLUcgJTsxWqmhwGiXegmJmrJj4um6A+jYdea0axWfh0fsf/bpCPeRJJM/mSeq2eOe63IfvRPj+vGPcUG+UUXecR11Q6uSSjpsgD2HdkKpy0sBAyUvOMR2N1ilDLKbbizYyvbO4puvt6nlPMe7GcYuN82cVbR/EErjxjbdhntFWKCTnFKCPFLFX1vxdTQcN3EQm/I+16iq6I/rHDflQifs6CH2kpV33xSdomZShXC58+8LR/3p7d4rFnM0KlWk1tdu/wPCGXSffQk8hnPXZuG+ObPznI08emU/OJQDi663t7j3DP48coZD3OGF/R8nud6iykJOMjrdy2HHEejhMKNOpo4wyb7ynO/3Mn1SnWwh++YXClGU3Dp5GuL+0UPjcjF1t3vbeR3u0km/G4YPMq7guMYlyN2qgkIx4+TTIq7hhn5iqUyhX2n5hZkPI0ugaXU1yVMHGinZKMDYHCMWnosaMWPu19TrE+fNrYU1xRyPJnv7CLD91wQepj0ihGvhNJqulwuHJpjum5cmgMO/UUnZF49OAEmZSQcDbjMVtRJmbSO7wUIt70fzx+FIBLd6xOfGycjNTmrDYcHSWdRXKuPW89P9p3nHJVG27+No4W2bF2mO/tPcIdPz3E7tNWdzVytNxZiDtxbcJtp0SXG7fzdaq5pItjfGL99Fyl7kJRe635OcW8m50W8xRbEdo4eukplsoVDk2UePrYdMPwKfiz9R54+gTloM4xSkOj2GReJdR7ik8dnUYVtjXYQbdK1hOePeG3ZouKdubnFJv/jbeuHuazb/sZrr9oU+pjaurT3uYUnVFUrSl7m01TuWHXFs7ZkNwhqBHRDWCipxgYpYmZMpOlmoLb9RFu11Ncv7JILiMcnZpL9BLBF7CVK1VOlsqpG5BipOn7PU8cZcfa4TAE3gzPkzAy1FB9Gpm80g4vifQnbtagYs9pa/juI4d4+JmTLbXKM2q0feUUkbeLyH3AuSLyo8jPXmApm4MvGu7C7XbdSfVUcbFMKaWxcJL6NJpTPDxR4tkgT9Isp7i2zih2t6NNlOnZKh/68oO86+/vaTioFvwQ3PRchZ8enOTE9Fyd+jbpwjEvp1iqtQCLE/UUf/zMSYC6qfad4tZw8xXb6/6OofrUhU9b3Hhceebahp9HPuvxy889jZecl96UvRsUgxZqpcg52U2VcpRoqiDJawqNYqkchk+h1pmp3UhHxqsN/E0TBGU9P6fYyFMcCjxFVeU/Hj/Gpdtb8xLB9xTdRraR+tQT6ej7ee6GlaExbJYmuOKMtUwGHu/VZ5lRbIdO4jV/D3wF+J/A+yK3n1TVI11ZVZ/jvuQuP5P0JYyPekmrd8olNgQPjGK5ynu/8CO++ZODQCvq00hOsZvh09j7Ts9VOHBihkMTs7XwaYqBuGiLLwj44ZPHOFkqc+6GlRybcm3eEkJckSG/0FhoE/UUH9x/gownHXk189aQ8ftY/tKVp9XdHuYU21Cftkp06kOvCHt7BufiTAueYqdEz7+0nCL4nuL0XE2sVguftm+st4wN8fjhqVRDn82IX2s7V2GkkC60OTEzx76j0xyaKLUcOgXfMLsax0bq0049RRHhugs28rd3Ph5uANK44vQ1gC8UuzBWG200pm2jqKrHgePAzSKyGjgbKIL/oanqt7q7xP4j7ikmhWuihg0CoU2SpxgYgTpPMWJQD0/OhgrW5kKb3oRP43McZ+YqTM6WmZotNw2fnrFuBSvyGb7700Oo+vmOhwOvLlFoEzYEj3mKCeHTQuQi/+DTJzhzfEVXPOQ1K/K88JzxeReeWvi0daFNP+HOv+m5CmP01lOMfg5p6lPwW/5NzVbC32tCm/Y/R5dXTPMUcxkvDOE3FNrMVWv5xO3pKs84GU9CgVijVEc+63VcN/gb157DjZdta1oes3X1EDvWDnPhltGmaRejno4z+yLyVuDdwFbgXuBn8Nu8vag7S+tf3EnmCqGTTtBcKLQJSjJmK+Ew1LrHNfAUZ8tVZuaq7Nw2xs6to1zSJJQTff1ehE9FQNW/qLpcUM0oJr+f5wkXbhnlu48cBmDTaDFy3/zHZ8KG4LWcokiykXd/M+cput3xQvmHX7ky8aIV72gzaEYxPlm+FJuD2U1cbV/Gk8TONC6n5wruXXcZF0Ztt3gfam3P0o2ihEYxrZlCIahTvP+p4xRzHue2EXnIeJHwaQNP8T0vPbeu4X87DOezLaUIRITP33JlWw0CDJ+FfKvfDVwOPK6q1wCXAAe7sqo+JxMLn8Y9Kf+2erFM2uSKpJKMaI3jzFyF7WuG+fANFzZUMLrXCkfl9EBo4wQHM3MVJmJt3xoZiIu3joYdcTauqnlfSTnSuKc4USqzIp88i9B5PnsPTbL/+Aznb+5ORdDqFflEIx/2Pi2VyWe9ttqQ9QPxyfIzPfQU3d8qzUtx3XGOTc0yOTs/fLogTzFF5RJVpKZ5isVgRuczJ2bYNDrUVtmEJ1GhTfq5ceGW5hvcbrBxtFhXUmS0xkK+ETOqOgMgIgVVfRg4t8lzWkJEtonIN0TkIRF5IJjIEX/MC0XkuIjcG/x8oBvv3QrzwqdJnqIXr1NMEdp4zlOMqk9rQhv/ea1/TM5wdrt4HwiHsk7PVpgo+TvuY8GU9EYX14u21kJQUU+xkdCmGigkp0rJbfQAztu0ivGVBf74az8Of+8l7nNRhUIbF8t+wXkN7ryd7fLczaT3Sgv5rypmg/aBs8H4tXj4tHNPMS1PGp0y00xoc+BEKXVYdRpZT0IPsJeN3Y3espBvxD4RGQO+BHxdRG4Dnu7OsigDv6mq5+GHZd8pIucnPO7bqror+Plwl967Ke6Ed0n1JE/RFejONRHauAttvfq05mW20pA4ilOgdlNo49bm5rdNlMqht3EkNIoNisAjif6N0fBpgqcVDvmt1IQ2ad04irkMv/L8M8JC+14bxYwntU1MFz3xxSIqtAE/fNqr+jX3umkek4iwdiTP4YmS3z94gXWKUPMU0zYsuZY8RY+ZcpVnT86ENaat4nkSpksaCW2M/qbjb7aqvkZVj6nqB4H3A38FvLobi1LV/ap6T/D/k8BDwJZuvHY3yMY8xbRdbS4yLDito80Lzhnn3S8+mzPHa10z6nOKrTckBt9TbHfsTjNqnqJ/kTg0MRve59qhNTISO9YOhzmkqKeYFD51G45K0O91slRuKEp43RXbWbMiz4ZVhZbryRaC+6y7qTxdLIZiOcVehk/dezUSh60dyXNwolQ3aWY411mdIvjnZ9aT9JKMiFFMyykWsxkqVWX/sZm2PcWMSCisG8BAghHQlRYaqvrNbrxOEiJyGn6+8q6Eu68UkR/ie6jvUdUHEp5/C3ALwPbt27uyplpJhv8FSPIU3e1zFQ0bCyftfseG8/zGtefMex74O/mZufZk82tHCm2P3WmGM7DrVzqjWArvOzLZXHQiIly81RfbRGfYpZVdZj2pdbSZrTTs2zic97uuuO5CvaaQyzA52ztj0kuc+GU64in2yrg7YVRS2Y1j7YpC2HTBhU9XFDJsGRvqqC2Zq1VM36RGwqdp6tPAOM9Wqm17ihlPKLUgtDH6m4WoT4vAO4Cr8RuDfwf4C5dn7AYiMgJ8Afh1VT0Ru/seYIeqTojI9fhh3LPjr6GqtwK3AuzevVvj93dCKzlF8I1bqVyNTC5v7ULqlKvuQt+Op/i6Pdu5oEuCE0c4m244RzHnxYxiKXhM4zVeftoa7tt3nFXFbDAHsZoaWvO8WrusqdnyvLl3cV5wznjLx7JQCgnh7kHB1Xq6NnV+79MelWRkm3uK60YK3LXXVyU7TzGb8fju+zoXsP/uK85LFZfUhU8btHlzrF/VpqcYySlaGcTgshBP8f8CJ4E/D36/Gfhb4LULXRSAiOTwDeJnVPWL8fujRlJVbxeRj4vIOlU91I33b4T7bjXqaAP+BdSJZaB14+bCp64erh2hzYVbRrterOsuJqNDOYZymXqjONXawN1ffcGZvHb3NkSE4XyWmbnZ1LxL1pO6GYxnjbfXjLqXuOPsVdF7L9mwsshIIcuPnzlJparMVbRnnqIXhDEbGYd1I/kw2tKt0oGXBRMxkog2v0iLPkS/oy4y0ioZTwgCHGYUB5iFGMVzVXVn5PdvBKHMBSN+7O+vgIeCuY1Jj9kIPKuqKiJ78POjh7vx/s1wISEntEmTgDuhjTOerYoHnJF1O/qlbuYbN4puZh34OcVcRppeBIq5TNhHdKiJCCMT8RSPTc61Ndy219RyioPnKXqecP6mVdz/1PGmnYi6wVAu07ALUzQHvBijjZwifKSQTVWHRj3nDW16itGQqYVPB5eFnIk/EJGfUdU7AUTkCuC73VkWVwFvAO4TkXuD234H2A6gqp8AbgTeLiJlYBq4SV2n4x4T1imWm4dP5yrVUNjQqqco4itX3fzBpS7APX/zKp539jou2jpKMZfhiSNT4X1Hp2bb9jZcqKyRUfQ9Gb9581hC04Olwh3rIBpF8D/Lz3//SQ4HYqkVPTy3irnGnqIbwAzJk2a6jTPQjcZ4RVXb69vOKUb/b0ZxUFmIUbwCeKOIPBH8vh14KGgWrqp6cacvrKrfITK8OOUxHwU+2ul7LASnHZhuUJLhbp8taxgiam/SttfWiKJesm6kwN++5QrAP4ZyZODwyZlyXSPyVnAXwLTddDYwiq77yOo+8hTzA6w+Bbhg8yqm5yp8+o7HANhz+tqevddQLtNEfVrzxBZj4+e+p40GPrt1rMhn2h76HBUVmac4uCzEKF7XtVUMGDWhTXqbN3f7bF34tHXvIp/1whFFS+0pRklaS7tek2vllRZa88Q3iq5xeH95ii6nOJie4gWb/Xzz3935ONvXDHPOht7la4u5xirodYvsKeZa8RRz9eVH7RDdG5ujOLh0bBRV9fFuLmSQcLvfUmDs0nbD+YzHXLkaPq4dFWnUU+yko36viK7FhTnbVWKG4dMGnmK5qmG3HMspdo+zN4yQD1TR156/oaet6oq5TDjtJIloTnFRwqeBJ9domLOLALSrPIX689nCp4NL299sEflO8O9JETkR/Jx0v3d/if2HC41Mz1Ua9sDMZTsT2oBvUF3j6V7J5jvBraWQ9cKLS7uhROdtpokdMhnf2B51nmIf9W8c9PBpLuNxzkbfO3zp+T2e35jzGrY7i/byHVoMoU22JrRJw52b7SpPIRY+NaM4sHQyOurq4N+FD64bUKJ1imnKU/AvQP68uMBTbGM3nMtIOLevnzxFZ9hXFrMUshmOTc21HUocbtIXc2Uhx4npudBT7Kec4qALbQB271jDwZMlLmtjVmAn3LxneziEOolc0MD+2NRcTwU/4ft5rYRP/XW0qzyFmNDGcooDy0KK918LfFVVT4rI7wGXAr+vqj/o2ur6lNBTnK0kDr91uDBVWIvVjqeY9cLQ01KXZERxx7CikA03BJ3mFNN205tGi+w/PlPLKa7oH09x0MOnAO+97jm860VntTUBohNu2NW8M+PaFXmOTc0tSt4824LQZqSQxRPC8qF2iJ7PFj4dXBbyrXh/YBCvBl4G/A3wie4sq79xApFSudqwx2guKN4Pe6S24VFFFa39ZBTdWkYK2XBD0HFJRspueuNokWdOzHB0apaMJ6l9KpeC/DIwikP5zKL0iW2FdSMFMp40jLh0C/ddbXQ+jQ7l+PyvXMkvXN5+S8iM1SkuCxZyJlaCf1+B397tNqB/4lw9JHryNxKZ5IPep50KbRz9qD71jWJnocSx4RxZT1LVp5tGixyZnOXZEyXGhnJ9NbcwDJ/20UZlkFk3UmC4iUq1W7RSkgF+S8JOvnNZ8xSXBQvZgj8lIn8JvAT4iIgUWJiRHRiiYZK0GkX/Pqkr3m9XaOMo9pFXEvUU3d+h3ZziTXu2s2vb6lQPc9OoH7p6+JkTfVWOAbVjHWRPsZ/YfdpqJkqL08w9G+YUe3NO1YdPe/IWxiKwkI/u54GvAdep6jFgNfBbXVlVn1PnKTY4+/NZj9myHz7NetLQgCY9F3zD2uvcTzs4b3ekmA3FEe2GT1cVc+w5fU3q/W681H8+O9FX5RiwPHKK/cSbrjqdv3nznkV5r1ZyigvBwqfLg4V8s18BfF1V/zMQ2nwc6Hkz7n4gk2ktfJrL+MX7M3PVthWkLv/RzWHB3cCVZKwoZBkOc4rdNRBuEPFspcrqPvMUB70k41Rm02iRXEY4fW37Y6lawYQ2y4NuC23+ojvL6m9a9hQzNaFNuzko51UW+yifCDVPcWUh6in2xigCjA71m6focormKQ4am8eGePDD13HR1u5OkXFEc4rmKQ4uJrTpgEzLOUVfaDMzVwkHvLaKKzTup8J9iPSGjKpPuyw6Gc5nw5l4/eYpWvh0sGknhdEuGfMUlwULOUOc0ObngdtPKaFNZBeYa6Q+DWoNJ0vltsOnzgPtp8J9iJVkBPWGvZDTu7zi6jabjfcaC58aaXjW5m1Z0E2hzRpOEaFNNEzSyCC4WrBHDk60XWs4CEZxuMOSjFZwRjFtivpSYZ6ikUbGwqfLgoU0BJ8Cvhj5fT+wvxuL6neiCfV8Nv3k37VtDIBHD042VFsmkcu6cof+MopR9els2e/U04v82sagLKOfWryB5RSNdCx8ujzoRkPwk0vZEFxErhORH4vIIyLyvsV6X3fSN/IUz9kwEnZvaV992p+e4unrVnDG+ArO27QqPLZehBLD8Gmf5RSfs3ElZ60fYUePFIzG4FLvKS7hQowFsaCG4CKyPvj/gW4vrBVEJAN8DLgW2Ad8X0T+WVUf7PV7Z0SooA0T99mMx8VbR7nz0SNtC2acse03oc34ygL/+psvBGD/sWmgt+HTfqtTPG3dCv7lv75gqZdh9CFWp7g86OhqJiIfFJGDwEPAwyJyUEQ+0N2ltcQe4BFVfVRVZ4HPATcsxhuHnmITg3Dpdn8SQdtCm2x/eopRwjrFHhju6y7cyO+94jyes/GUHcZiDBhWp7g86CR8+hvAVcAeVV2rqmuAK4CrgvsWky3Ak5Hf9wW3hYjILSJyt4jcffDgwa69sTvpm0m8LwmMYrtCmzB82md1ilFcMfTm0fYnCjRjZTHHW593hs2lMwYG6326POhki/9G4GZV3etuUNVHgV8M7ltMks68ulHfqnqrqu5W1d3j4+Nde2N3zjcLHTqxTadGsZ+l/xtWFbnn/ddyxRlrl3ophrHkeKY+XRZ0oj7Nqeq8dm6qelBEFlsVsQ/YFvl9K/D0Yrxxq57i+MoCb3ve6Vxz7vq2Xt+1eetnTxF8j84wjPqconmKg0snRnG2w/t6wfeBs0XkdOAp4CbgdYvxxhnPN4bNcooAv/uK89t+feeB9lvvU8Mwkonuj9NmhRr9TydGcWdK6YUAxYTbe4aqlkXkXfhNBDLAp1T1gcV4b/cF6FXbqFpOsb/Up4ZhJOM2ygCefW0Hlk5KMvrKdVHV24HbF/t93U6wFU+xE/q1TtEwjGTqPEULnw4stp/pEC8s3u/Nye96qvZbRxvDMJLxrE5xWWBGsUOyLQptOqVfe58ahpGMtXlbHphR7BCvxeL9TnE9Vdst5TAMY2moM4rmKQ4sZhQ7xJ30PRfamFE0jIGgrs2beYoDixnFDmm1zVunXLxljFdevKlnU8INw+gu7ppgodPBpuPRUac6rUzJWAijwzk++rpLe8eNENoAAApYSURBVPLahmF0n9AoWuh0oDFPsUN67SkahjFYuGuC1SgONvbxdYjX45yiYRiDhbsmWDnGYGNX9A4xT9EwjCgWPl0e2BW9Q2rqU/sCGIZRq1025elgY0axQ9yusBdT5w3DGDw8U58uC+yK3iGtjo4yDOPUIGM5xWWBXdE7pNcdbQzDGCxqdYpLvBBjQdjH1yEulWieomEYYEKb5YJd0TuknSHDhmEsfzImtFkW2BW9Q5yD2KuONoZhDBYul2hCm8Gm767oIvJ6EflR8HOHiOxMedynRWSviNwb/OxazHWa0MYwjCgWPl0e9GPv073AC1T1qIi8HLgVuCLlsb+lqv+4eEur4YmQ8cR2hYZhABY+XS70nVFU1Tsiv94JbF2qtTQi64kV7huGEWKe4vKg32N/bwG+0uD+/xGEWf9MRApJDxCRW0TkbhG5++DBg11bmOeJ5RMNwwgJ6xTNUxxo+vaqLiLX4BvF96Y85LeB5wCXA2vSHqeqt6rqblXdPT4+3rX1ZURMeWoYRoibjmF75cGmL8KnIvJO4G3Br9cD64BPAi9X1cNJz1HV/cF/SyLy18B7er7QCNc8Zz2rV+QX8y0Nw+hjsoFVtPDpYNMXRlFVPwZ8DEBEtgNfBN6gqj9Je46IbFLV/SIiwKuB+xdlsQHXX7SJ6y/atJhvaRhGH+OipmJGcaDpC6MY4wPAWuDjwclVVtXdACJyO/BWVX0a+IyIjAMC3Av86hKt1zAMAxHBE6tTHHT6ziiq6luBt6bcd33k/y9atEUZhmG0QMYTC58OOJYSNgzD6BIZT0LBjTGY2MdnGIbRJTJiDT0GHTOKhmEYXcLzxOYpDjhmFA3DMLqEtX4cfMwoGoZhdImsCW0GHjOKhmEYXcITsTZvA44ZRcMwjC5hJRmDjxlFwzCMLuGZ+nTgMaNoGIbRJbIZC58OOn3X0cYwDGNQefeLz2bT6NBSL8NYAGYUDcMwusTPXdqXM9GNNrDwqWEYhmEEmFE0DMMwjAAzioZhGIYRYEbRMAzDMAIG1iiKyAtF5LiI3Bv8fGCp12QYhmEMNoOuPv22qr5yqRdhGIZhLA8G1lM0DMMwjG4z6EbxShH5oYh8RUQuWOrFGIZhGIPNIIdP7wF2qOqEiFwPfAk4O/4gEbkFuCX4dUJEfryA91wHHFrA8weNU+14wY75VMGOuT12dHMh/Yyo6lKvoWVE5J3A24Jfr1fVpyP3PQbsVtWenegicreq7u7V6/cbp9rxgh3zqYIds5HGQIVPVfVjqrpLVXcBVRF/RouI7ME/lsNLukDDMAxjoBnk8OmNwNtFpAxMAzfpILm9hmEYRt8xsEZRVT8KfHSR3/bWRX6/peZUO16wYz5VsGM2EhmonKJhGIZh9JKByikahmEYRi8xo2gYhmEYAWYUW0BErhORH4vIIyLyvqVeT7dpdnwi8noR+VHwc4eI7FyKdXaTVj9TEblcRCoicuNirq8XtHLMQU/he0XkARH55mKvsdu0cG6PisiXgyYgD4jIm5Zinb1ERD4lIgdE5P6lXstAoKr20+AHyAA/Bc4A8sAPgfOXel2LeXzAc4HVwf9fDty11OtejM80eNy/ArcDNy71uhfhcx4DHgS2B7+vX+p1L8Ix/w7wkeD/48ARIL/Ua+/y3+H5wKXA/Uu9lkH4MU+xOXuAR1T1UVWdBT4H3LDEa+omTY9PVe9Q1aPBr3cCWxd5jd2m1c/014AvAAcWc3E9opVjfh3wRVV9AkBVB/24WzlmBVYGNc8j+EaxvLjL7C2q+i384zJawIxic7YAT0Z+3xfctlxo9/jeAnylpyvqPU2PWUS2AK8BPrGI6+olrXzO5wCrReTfROQ/ROSNi7a63tDKMX8UOA94GrgPeLeqVhdneUY/MrB1iouIJNy2nOpYWj4+EbkG3yhe3dMV9Z5Wjvl/A+9V1UrQOGnQaeWYs8BlwIuBIeDfReROVf1JrxfXI1o55pcB9wIvAs4Evi4i31bVE71enNGfmFFszj5gW+T3rfi7yuVCS8cnIhcDnwRerqqD3k6vlWPeDXwuMIjrgOtFpKyqX1qcJXadVo55H3BIVSeBSRH5FrATGFSj2Moxvwn4Q/WTb4+IyF7gOcD3FmeJRr9h4dPmfB84W0ROF5E8cBPwz0u8pm7S9PhEZDvwReANA+w1RGl6zKp6uqqepqqnAf8IvGOADSK0dh7fBjxPRLIiMgxcATy0yOvsJq0c8xP4njEisgE4F3h0UVdp9BXmKTZBVcsi8i7ga/hqtk+p6gNLvKyukXZ8IvKrwf2fAD4ArAU+HnhOZR3gbvstHvOyopVjVtWHROSrwI+AKvBJVR1YGX+Ln/PvA58Wkfvww63v1R5O2lkKROSzwAuBdSKyD/jvqvpXS7uq/sXavBmGYRhGgIVPDcMwDCPAjKJhGIZhBJhRNAzDMIwAM4qGYRiGEWBG0TAMwzACzCgaRgpBu7OXxW77dRH5eIPnPCYi60RkTETe0ftVgoi8WkQ+EEy4+PfYfVkReVZENonIH4vIixZjTYYxqJhRNIx0Potf8B3lpuD2ZowBi2IUgf8GfBz4FrBVRE6L3PcS/OkI+4E/B5bd6DPD6CZmFA0jnX8EXikiBYDA2GwGviMiN4vIfSJyv4h8JOG5fwicGcwm/CMRGRGR/yci9wTPC6c1iMj7ReRhEfm6iHxWRN4T3H6miHw1aM79bRF5TvxNROQcoKSqh4JG1v8A/ELkIaERV9XHgbUisrELfxvDWJaYUTSMFIIer98Drgtuugn4PLAJ+Ah+E+ldwOUi8urY098H/FRVd6nqbwEzwGtU9VLgGuBPxGc38F+AS4Cfw++56rgV+DVVvQx4D743GOcq4J7I76F3Gxjz6/HHXznuCZ5jGEYC1ubNMBrjjMxtwb9vBi4H/k1VDwKIyGfwB7k26o0qwB+IyPPxW6htATbgTxy5TVWng9f6cvDvCP5w53+ITOkoJLzuJuCg+0VVvx94pefij0S6MzILE/zZkJtbPnrDOMUwo2gYjfkS8KcicikwpKr3BA3S2+X1+JPdL1PVORF5DCiSPN4I/CjOMVXd1eR1p4HR2G2fwzfg5zE//1kMnmMYRgIWPjWMBqjqBPBvwKeoGZi7gBcEKtMMcDPwzdhTTwIrI7+PAgcCg3gNsCO4/TvAq0SkGHiHrwje9wSwV0ReCxCEWncmLPEh4KzYbZ8FfhE/vBufCnEOMLBNvg2j15hRNIzmfBZ/ruDnAAIl528D3wB+CNyjqrdFnxDkI78bCHH+CPgMsFtE7sb3Gh8OHvd9fMP1Q/zxXHcDx4OXeT3wFhH5IfAAcAPz+RZwiURirKr6IDAF/GswGxEAEcnhG9C7O/9TGMbyxqZkGMYSIyIjqjoRzDD8FnCLqt7T7HmR5/8f4Muq+i9NHvca4FJVff/CVmwYyxfzFA1j6blVRO7FV4Z+oR2DGPAHwHALj8sCf9Lu4gzjVMI8RcMwDMMIME/RMAzDMALMKBqGYRhGgBlFwzAMwwgwo2gYhmEYAWYUDcMwDCPg/wMcu1AdfLCxZwAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_dataset(datasaver.dataset)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'parallel_capacitance': 'No status error occurred.',\n",
" 'dissipation_factor': 'No status error occurred.'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b1500.cmu1.run_sweep.status_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### IV Sweep"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section explains the IV Staircase sweep measurements. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enable the channels. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.enable_outputs()\n",
"b1500.smu2.enable_outputs()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Always good to do for the safety of the measured sample\n",
"b1500.smu2.voltage(0)\n",
"b1500.smu1.voltage(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setting up smu1 and smu2 for running the staircase sweep. One of the smu's is used for sweep. Both the smu's are used for acquiring data. It is possible to acquire data with more SMUs, that depends on the measurement mode (see below), so refer to the instrument manual for information on how many 'channels' can be measured with which measurement mode. In the setup below, smu1 is used to sweep over the sweep voltages -3 to 3 in 201 steps."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.setup_staircase_sweep(\n",
" v_src_range=constants.VOutputRange.AUTO,\n",
" v_start=3,\n",
" v_end=-3,\n",
" n_steps=201,\n",
" av_coef=5,\n",
" step_delay=0.225,\n",
" abort_enabled=constants.Abort.ENABLED,\n",
" i_meas_range=constants.IMeasRange.FIX_10nA,\n",
" i_comp=1e-8,\n",
" sweep_mode=constants.SweepMode.LINEAR,\n",
" # and there are more arguments with default values\n",
" # that might need to be changed for your\n",
" # particular measurement situation\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`smu2` is kept at constant voltage and at different compliance and measurement range settings."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu2.voltage(10e-3)\n",
"b1500.smu2.enable_filter(True)\n",
"b1500.smu2.measurement_operation_mode(constants.CMM.Mode.COMPLIANCE_SIDE)\n",
"b1500.smu2.current_measurement_range(constants.IMeasRange.FIX_10uA)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`b1500.set_measurement_mode` is used to define measurement mode and the channels from which data is extracted from. Here, channels correspond to SMU1 and SMU2 respectively - the SMU which is setup to run the sweep needs to go **FIRST**."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"b1500.set_measurement_mode(\n",
" mode=constants.MM.Mode.STAIRCASE_SWEEP,\n",
" channels=(b1500.smu1.channels[0], b1500.smu2.channels[0]),\n",
")\n",
"# SMUs have only one channel so using `channels[0]` is enough\n",
"# This might be improved in the future for better clarity and user convenience."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`run_iv_staircase_sweep` is used to run the sweep"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting experimental run with id: 2329. \n"
]
}
],
"source": [
"initialise_database()\n",
"exp = load_or_create_experiment(\n",
" experiment_name=\"dummy_iv_sweep_measurement\", sample_name=\"no sample\"\n",
")\n",
"meas = Measurement(exp=exp)\n",
"\n",
"# As per user needs, names and labels of the parameters inside the\n",
"# MultiParameter can be adjusted to reflect what is actually being\n",
"# measured using the convenient `set_names_labels_and_units` method.\n",
"# The setpoint name/label/unit (the independent sweep 'parameter')\n",
"# can be additionally customized using `set_setpoint_name_label_and_unit`\n",
"# method.\n",
"# Below is an example of using `set_names_labels_and_units`:\n",
"b1500.run_iv_staircase_sweep.set_names_labels_and_units(\n",
" names=(\"gate_current\", \"source_drain_current\"),\n",
" labels=(\"Gate current\", \"Source-drain current\"),\n",
")\n",
"# The number of names (and labels) MUST be the same as the number of channels,\n",
"# and the order of the names should match the order of channels, as passed to\n",
"# `set_measurement_mode` method.\n",
"\n",
"meas.register_parameter(b1500.run_iv_staircase_sweep)\n",
"\n",
"with meas.run() as datasaver:\n",
" res = b1500.run_iv_staircase_sweep()\n",
" datasaver.add_result((b1500.run_iv_staircase_sweep, res))\n",
"\n",
"# In production code, remeber to revert the names/labels of the\n",
"# run_iv_staircase_sweep MultiParameter in order to avoid confusion."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([,\n",
" ],\n",
" [None, None])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_dataset(datasaver.dataset)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'gate_current': 'No status error occurred.',\n",
" 'source_drain_current': 'No status error occurred.'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b1500.run_iv_staircase_sweep.status_summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performing phase compensation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The phase compensation is performed to adjust the phase zero.\n",
"\n",
"One must take care of two things before executing the phase compensation. First, make sure that all the channel outputs are enabled else instrument throws an error. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.run_iv_staircase_sweep.measurement_status()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second, the phase compensation mode must be set to manual. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.phase_compensation_mode(constants.ADJ.Mode.MANUAL)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the phase compensation can be performed as follows. This operation takes about 30 seconds (the visa timeout for this operation is set via `b1500.cmu1.phase_compensation_timeout` attribute)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"b1500.cmu1.phase_compensation()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `phase_compensation` method also supports loading data of previously performed phase compensation. To use that, explicitly pass the operation mode argument:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.phase_compensation(constants.ADJQuery.Mode.USE_LAST)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performing Open/Short/Load correction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Set and get reference values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the following method to set the calibration values or reference values of the open/short/load standard. Here, we are using open correction with Cp-G mode. The primary reference value, which is the value for Cp (in F), is set to 0.00001, and the secondary reference value, which is the value of G (in S), is set to 0.00002. These values are completely arbitrary, so please change them according to your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.set_reference_values(\n",
" corr=constants.CalibrationType.OPEN,\n",
" mode=constants.DCORR.Mode.Cp_G,\n",
" primary=0.00001,\n",
" secondary=0.00002,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can retrieve the values you have set for calibration or the reference values of the open/short/load standard in the following way:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.get_reference_values(corr=constants.CalibrationType.OPEN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Add CMU output frequency to the list for correction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add to the list of frequencies supported by the instrument to be used for the data correction. The frequency value can be given with a certain resolution as per Table 4-18 in the programming manual."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.frequency_list.add(1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clear CMU output frequency list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clear the frequency list for the correction data measurement using the following methods. Correction data will be invalid after calls to these methods, so you will have to again perform the open/short/load correction.\n",
"\n",
"There are two modes in which you can clear the frequency list. First is clearing the list of frequencies:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.frequency_list.clear()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Second is clearing the list of frequencies and also setting it to a default list of frequencies (for the list of default frequencies, refer to the documentation of the ``CLCORR`` command in the programming manual):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.frequency_list.clear_and_set_default()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Query CMU output frequency list\n",
"\n",
"It is possible to query the total number of frequencies in the list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.frequency_list.query()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is also possible to query the values of specific frequencies using the same method by specifying an index within the frequency list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.frequency_list.query(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Open/Short/Load Correction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As per description in the programming guide, we first set the oscillator level of the CMU output signal."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set oscillator level\n",
"b1500.cmu1.voltage_ac(30e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To perform open/short/load correction connect the open/short/load standard and execute the following command to perform and enable the correction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.perform_and_enable(corr=constants.CalibrationType.OPEN)\n",
"# b1500.cmu1.correction.perform_and_enable(corr=constants.CalibrationType.SHORT)\n",
"# b1500.cmu1.correction.perform_and_enable(corr=constants.CalibrationType.LOAD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you would only like to perform the correction but not enable it, you can use separate methods `perform` and `enable`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To check whether a correction is enabled, use the following method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.is_enabled(corr=constants.CalibrationType.OPEN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To disable a performed correction, use the following method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.cmu1.correction.disable(corr=constants.CalibrationType.OPEN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SMU sourcing and measuring\n",
"The simplest measurement one can do with the B1500 are *High Speed Spot Measurements*. They work independent of the selected *Measurement Mode*.\n",
"\n",
"The `voltage` and `current` Qcodes Parameters that the SMU High Level driver exposes will execute *High Speed Spot* measurements. Additionally, there are functions that let the user specify the output/measure ranges, and compliance limits.\n",
"\n",
"To source a voltage/current do the following:\n",
" 1. Configure source range, and (optionally) compliance settings\n",
" 2. Enable the channel\n",
" 3. Force the desired voltage\n",
" 4. (optionally) Disable the channel\n",
"\n",
"**Note: The source settings (Step 1) are persistent until changed again. So for sucessive measurements the configuration can be omitted.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.enable_outputs()\n",
"\n",
"b1500.smu1.source_config(output_range=constants.VOutputRange.AUTO, compliance=0.1)\n",
"\n",
"b1500.smu1.voltage(1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To measure do the following:\n",
" 1. Configure the voltage or/and current measure ranges\n",
" 2. Enable the channel (if not yet enabled)\n",
" 3. Do the measurement\n",
" 4. (optionally) Disable the channel\n",
"\n",
"**Note: The measure settings (Step 1) are persistent until changed again. So for sucessive measurements the configuration can be omitted.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.smu1.i_measure_range_config(i_measure_range=constants.IMeasRange.MIN_100mA)\n",
"b1500.smu1.v_measure_range_config(v_measure_range=constants.VMeasRange.FIX_2V)\n",
"\n",
"b1500.smu1.enable_outputs()\n",
"\n",
"cur = b1500.smu1.current()\n",
"vol = b1500.smu1.voltage()\n",
"\n",
"b1500.smu1.disable_outputs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting up ADCs to NPLC mode\n",
"\n",
"Both the mainframe driver and SMU driver implement convenience methods for controlling integration time of the *High Speed Spot* measurement, which allow setting ADC type, and setting the frequenty used NPLC mode."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the following methods on the mainframe instance to set up the ADCs to NPLC mode:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set the high-speed ADC to NPLC mode,\n",
"# and optionally specify the number of PLCs as an arugment\n",
"# (refer to the docstring and the user manual for more information)\n",
"b1500.use_nplc_for_high_speed_adc(n=1)\n",
"\n",
"# Set the high-resolution ADC to NPLC mode,\n",
"# and optionally specify the number of PLCs as an arugment\n",
"# (refer to the docstring and the user manual for more information)\n",
"b1500.use_nplc_for_high_resolution_adc(n=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then use the following methods on the SMU instances to use particular ADC for the particular SMU:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use high-speed ADC\n",
"# with the settings defined above\n",
"# for the SMU 1\n",
"b1500.smu1.use_high_speed_adc()\n",
"\n",
"# Use high-resoultion ADC\n",
"# with the settings defined above\n",
"# for the SMU 2\n",
"b1500.smu2.use_high_resolution_adc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error Message\n",
"\n",
"The error messages from the instrument can be read using the following method. This method reads one error code from the head of the error queue and removes that code from the queue. The read error is returned as the response of this method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"b1500.error_message()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, the response message contains an error number and an error message. In some cases the error message may also contain the additional information such as the slot number. They are separated by a semicolon (;). For example, if the error 305 occurs on the slot 1, this method returns the following response. 305,\"Excess current in HPSMU.; SLOT1\" \n",
"\n",
"If no error occurred, this command returns 0,\"No Error\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Low Level Interface\n",
"\n",
"The Low Level Interface provides a wrapper around the FLEX command set. Multiple commands can be assembled in a sequence. Finally, the command sequence is compiled into a command string, which then can be sent to the instrument. \n",
"\n",
"Only some very minimal checks are done to the command string. For example some commands have to be the *last* command in a sequence of commands because the fill the output queue. Adding additional commands after that is not allowed.\n",
"\n",
"As an example, a \"voltage source + current measurement\" is done, similar as was done above with the high level interface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mb = MessageBuilder()\n",
"\n",
"mb.cn(channels=[1])\n",
"mb.dv(chnum=1, voltage=1.5, v_range=constants.VOutputRange.AUTO, i_comp=0.1)\n",
"mb.ti(chnum=1, i_range=constants.IMeasRange.FIX_100uA)\n",
"mb.cl(channels=[1])\n",
"\n",
"# Compiles the sequence of FLEX commands into a message string.\n",
"message_string = mb.message"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(message_string)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The message string can be sent to the instrument. To parse the response of this spot measurement command, use the `KeysightB1500.parse_spot_measurement_response` static method.\n",
"\n",
"`parse_spot_measurement_response` will return a `dict` that contains the measurement value together with the measurement channel, info on what was measured (current, voltage, capacitance, ...), and status information. For a detailed description, see the user manual."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = b1500.ask(message_string)\n",
"\n",
"KeysightB1500.parse_spot_measurement_response(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `MessageBuilder` object can be cleared, which allows the object to be reused to generate a new message string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mb.clear_message_queue()\n",
"# This will produce empty string because MessageBuilder buffer was cleared\n",
"mb.message"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `MessageBuilder` provides a *fluent* interface, which means every call on the `MessageBuilder` object always returns the object itself, with the exeption of `MessageBuilder.message` which **returns the compiled message string**.\n",
"\n",
"This means that the same message as in the first example could've been assembled like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = b1500.ask(\n",
" MessageBuilder()\n",
" .cn(channels=[1])\n",
" .dv(\n",
" chnum=1,\n",
" voltage=1.5,\n",
" v_range=constants.VOutputRange.AUTO,\n",
" i_comp=0.1,\n",
" )\n",
" .ti(chnum=1, i_range=constants.IMeasRange.FIX_100uA)\n",
" .cl(channels=[1])\n",
" .message\n",
")\n",
"\n",
"KeysightB1500.parse_spot_measurement_response(response)"
]
},
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"cell_type": "code",
"execution_count": null,
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