DMU 0.4.2

This package is used to store commonly used functions on pip for the sake of easy pulling for other code. Features utilities for IV characterisation, lumerical data analysis and geometry instanciation, SEM stitching and other tools.

ATTENTION

This is a code-storage repository for small functions, big functions, GUI's and other things I have developed for every-day electrical characterisation and simulation work. You'll find things from figure handling, colourmaps and named colours (combining tab20b and tab20c, for instance), to data extractors, data save-tools, json serialisation, pickle jars, ideality GUI plotters, you name it! Code from my other lumerical specific package, called LDI, has been included in its entirety, where slightly more extended documentation can be found here, including some setup examples of their intended use, but you can consider the vast majority of those functions outdated or defunct.

Most functions should be sufficiently commented to understand what they do, and a lot of functions are redunant.

Installation and Updating

install pip install DMU update pip install DMU --upgrade

Package Structure

DMU contains several modules, all which should serve a specific function (with some overlap)

• utils.py

  • Data importers (matloader, for instance, can pair .mat files with json files and collapse contents to a dict structure)
  • Automatic data plotting for Keithley and Nanonis tools
  • Lumerical functions

• utils_utils.py

  • Contains additional miscellaneous utility functions. Mostly unused.

• plot_utils.py

  • Generally contains tools that assist in figure plotting, wherein some examples include:
  • adjust_ticks - increase or reduce number of ticks along axis to
  • align_axis_zeros - provide a list of twinx axes in a plot, and it will force the zero to be aligned in all of them. useful for visualisation.
  • And many, many other functions that assist in plotting.

• sem_tools.py

  • Specialised package to import and modify SEM TIFF images. The main purpose of most of it being to:
  • Create uniform scalebars over many different images cropped to different sizes
  • Create nice looking inserts into images, highlighting both the area of the larger overview image and boxing in the insert at a specified location. Examples will be provided at a later date.
  • Perform rudimentary post-processing on the images, like expanding the dynamic range, modifying the contrast, sharpness and brightness.

DMIdeal

This is a GUI whose development was mainly focused on folder-scrubbing and sorting device data from our excessively expansive electrical characterisation data for the express purpose of fitting, and cataloguing ideality data. It is compatible with the .xls data output from the Clarius+ software provided with a setup using a Cascade 11000B probe station with a Keithley 4200A-SCS parameter analyser.

Folder structure requirements for this tool

Unfortunately, much of this project is hard-coded to support our device architecture and measurement structure. I hope to be able to rewrite this to be more general in the future, but for now this works for our 2NW/1NW measurements. This means that if you have 2 probe measurements, you can adapt this repo for your own purposes with minor modifications to the retrieval code. Otherwise, the python data importer for this .xls data works on any Clarius+ data file (this is in DMU.utils.Keithley_xls_read(file), and works as a standalone)

how the data storage is structured The script crawls all containing folders of the root directory, and collects any and all .xls files it finds. For each .xls file, it also looks for a log-file in the same directory, reading the runID of the saved .xls data and seeing if the same runID has been logged in the logbook. An logbook and measurement data can be found at the bottom of this data structure.
When selecting the data storage folder, we choose the root directory (in this case, it is ExampleData) within which all devices are stored. Then, the structure should be: DeviceRoot/DeviceName/DeviceName_MaterialName/DeviceName_SubdeviceName/(Data.xls) (as an example, we have used: DFR1_RAW_DATA\DFR1_GK\DFR1_GK_AIR\2024_10_10_DFR1_GK_TL2(data is here))

The GUI is heavily reliant on a logbook system (see example data), since we look to categorise: device-subdevice-Nanowire ID which can't be collected from the keithley data alone, but must be manually specified. In the future, we could have injected this data into the settings file for each run to avoid needing to have a logbook, but no matter. We use a Probe station - Cascade 11000B which has 4 SMUs, and 4 probes. For 2NW measurements, each NW needs two probes, so our logbook reflects that.

Logbook Example (also see example data)

RUN No. Device SMU1 SMU4 SMU2 SMU3 Light Room Light Microscope V @ I = 1e-6 Range Comment NW1 NW2 p-i-n; detector n-i-p; emitter 900 DFR1_GG_BR1 sweep [-4.5,4.5] common ground NA NA FALSE FALSE 10uA

So which one of these NEED to be present for the script to work?

• Data you want to keep NEEDS a RUN No. corresponding to the Run No of exported data from clarius.
• Device needs to be filled, otherwise we can't populate the list. The format needs to be: DeviceName_Subdevicename (hyphens work too). If you don't have any "subdevices", make the devicename anything else, like the type of device.
• SMU1/SMU2/SMU3/SMU4 all need a separate entry, the order does not matter, so long as each pair in C-D and E-F correspond to paired probes in an IV
• NW1/NW2 needs to be defined, but for single device measurements you can just ignore E-F and only use the NW1
• The contents of all cells can be empty. Ideally, Light Room and Light Microscope are always FALSE unless it matters to you. Comment can contain ANYTHING and will be stored in the dict file created later, in case you want to retrieve it.

Note that empty rows are ok, Run no.s that have no corresponding runID in the data also are also fine. The text in all filds are processed as strings. Only rows with a Run No are processed by the data importer. The script currently hard-codes "NW1" and "NW2" detecton, but as long as you have both present, you can ignore filling data for any other field.

So! Fill the logbook with RUN no. from the data that is saved into the .xls file that clarius exports, and place the log file in the same folder.

The log file needs to share the same name within the first three underscores as the rest of the data, for example:

DFR1_GG_BR1_LOG wil be checked for all files DFR1_GG_BR1_6_4Term, DFR1_GG_BR1_5_4Term, DFR1_GG_BR1_4_4Term, DFR1_GG_BR1_2_2Term But not for DFR1GG_BR1_2_2Term or GG_BR1_2_2Term.

How to use the GUI for idealiy fitting

• Setting up data storage directories

  • First, make sure your user mode is set to ideality.
  • Then, select your Data Storage folder - This is where json file versions of your xls data will be stored and loaded from. Your personal settings are saved in Session under your user name (found with os.getlogin()).
  • After this, you should select your root data directory as advised above. To test the GUI, simply pick "DeviceRoot" in "Example Data". Then choose "Crawl Data Storage and Merge Dicts" and it will populate the device/subdevice/runID lists.
  • Data is saved every time you change subdevice or device, but you can also mannually save.

• For fitting the ideality

  • In the fitting panel, click "Activate I0 Range" to activate the I0 cursor. Use this to highlight the reverse saturation current regime, which provides a value for the initial guess of $I_0$. The cursor will then automatically swap to the "Activate Idealty Fit range", where another range cursor can be used to highlight the ideal regime of the IV curve.
  • "Fit data" can now be presesd to run a scipy.optimize script, plotting and saving a new value for the plotted ideality. This range is very sensitive, so try to modify it until your ideality fit looks good.
  • Another implementation attempts to use the fitted ideality and $I_0$ as the initial guess to analytically attempt to solve for the series resistance $R_s$. $R_s$ is found both through the linear fit of the series resistance regime at higher voltages, and through a rewritten Shockley diode equation, solving for $R_s$ and $n$, where each point on the IV curve is used to first evaluate $R_s$ with the scipy.root minimisation function, where $n$ and $I_0$ are set to their initial guesses. This is then repeated for the $n$ formula, choosing the evaluated $n$ in the ideality regime. Finally, all three parameters are evaluated per point to see if the Shockley diode equation converges to the current. As evidenced by the red fit in the Figure, all values eventually converge.
  • Note that the limited voltage range in the ideality measurements causes the linear fit estimate of $R_s$ to always be much larger than the one estimated with the analytical fit.

How packages can be imported

import DMU -> DMU.utils, DMU.plot_utils, DMU.utils_utils, DMU.sem_tools etc

how I usually import packages:

from DMU import utils as dm

from DMU import plot_utils as dmp

from DMU import utils_utils as dmuu

from DMU import sem_tools as dms

from DMU import graph_styles as gss