Presentation + Paper
3 October 2022 On the modeling of organic electrochemical transistors
Lukas M. Bongartz, Matteo Cucchi, Karl Leo, Hans Kleemann
Author Affiliations +
Abstract
Due to their synaptic functionality based on interacting electronic and ionic charge carriers, organic electrochemical transistors (OECTs) appeal as highly attractive candidates for a new generation of organic neuromorphic devices. Despite their acknowledged application potential, little is still known about the underlying physics and traditional transistor models fail to accurately describe the phenomena observed. This deficiency comes in part from the fact that such models are largely based on an electrostatic approach for metal-oxide-semiconductor field-effect transistors (MOSFETs), which is a very strong abstraction to the volumetric and complex processes in OECTs. On the other hand, material studies reveal the potential of an alternative approach, taking into account the electrochemical processes by means of thermodynamics and thus considering the OECTs intricacy. These two approaches oppose each other in explaining OECTs, neither of which can claim a comprehensive explanation of the transistor on its own so far. A unification of the two sides, on the other hand, could come much closer to a substantial explanation and provide a more accurate picture of reality. After giving a short overview of the most significant concepts of the two explanatory directions, a framework is presented that might come very close to this merger, as it accurately reproduces essential transfer properties of OECTs in terms of thermodynamics for the first time.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lukas M. Bongartz, Matteo Cucchi, Karl Leo, and Hans Kleemann "On the modeling of organic electrochemical transistors", Proc. SPIE 12210, Organic and Hybrid Sensors and Bioelectronics XV, 1221005 (3 October 2022); https://doi.org/10.1117/12.2633291
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KEYWORDS
Transistors

Thermodynamics

Data modeling

Instrument modeling

Ions

Diffusion

Switching

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