Automated OVSED Parameter Extraction Using Gradient Descent: A Python Tool for Cross-Technology Benchmarking of Organic Thin-Film Transistors

Organic thin-film transistors (OTFTs) utilize organic semiconductors to generate electronic responses. Operating with three terminals, these devices control the current flow between the source and drain electrodes by applying a voltage to a gate electrode. OTFT technology is evaluated through reports of key parameters, such as threshold voltage, charge carrier mobility, and series resistance. However, conventional parameter extraction methods designed for silicon transistors may yield inaccurate results when applied to OTFTs. Here we suggest the use of a set of simplifying analytic equations that were previously derived from the virtual-source emission-diffusion theory and that are capable of describing the electrical characteristics of OTFTs and their peculiarities. The reduced number of modeling parameters allows for an automated extraction from measured output and transfer curves leading to a more robust benchmark strategy. We describe a Python platform for the parameter extraction and provide exemplary benchmarks.