ofet_import

Import characterisation data from Organic Thin Film Transistors (O-FETs) from available online sources, into a common python data structure.

Usage

import os

os.environ['PATH_TO_TRANSISTOR_SWEEPS'] = r"path/to/Sweep data CNR"
from iit_cnr_2025 import transistors as transistors_iit_cnr_2025

os.environ['PATH_TO_TRANSISTOR_SWEEPS'] = r"path/to/TCASi-ONC"
from purdue_2022 import transistors as transistors_purdue_2022

Then consider using https://github.com/event-driven-robotics/ivplot to plot the transistors, example code:

from plot import plot

for transistor_name, transistor in transistors_purdue_2022.items():
    label = '_'.join(('purdue', transistor_name))
    fig = plot(transistor['sweeps'], marker='o', label=label, markersize=6)

##Description

Each script is an importer for a different source of raw data.

Each script describes where to dowload the raw data, and then, running the script, the output will be a dict in this form:

{
    "<transistor_name>": {
        "sweeps": [...],
        "params": {...},
        "data": pandas.DataFrame(...)
    },
    ...
}

'params' may look like:

transistor['params'] == {
    'length': <float or '?'>,
    'width': <float or '?'>,
    'polarity': 'p' or 'n' or '?',
    'permittivity_insulator_relative': 3.15,
    'thickness_insulator': 700e-9,
    'temperature': 290,
}

Units are always SI units, so metres, kelvin etc.

Each sweep in the a list of 'sweeps' is a dict in this form:

{ 'datetime': parsed['datetime'], # string from filename (first 6 tokens) 'type': parsed['sweep_type'], # 'g' for Vg sweep or 'd' for Vd sweep, or 'both' for mixed data. 'data': df # DataFrame with columns ['vgs', 'vds', 'ids'] }