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Effects of Charge Traps on Hysteresis in Organic Field-Effect Transistors and Their Charge Trap Cause Analysis through Causal Inference Techniques

Journal

SENSORS
Volume 23, Issue 4, Pages -

Publisher

MDPI
DOI: 10.3390/s23042265

Keywords

hysteresis; charge trapping; organic semiconductors; data analysis; causal inference

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Hysteresis in organic field-effect transistors is caused by bias stress effects, which result in variations in the drain-source current when sweeping the gate voltage. Charge trapping, commonly found in interface states or semiconductors, is one of the main factors contributing to hysteresis. Despite extensive research, accurately measuring and characterizing charge traps remains challenging, and their impact on the hysteresis of organic transistors is still largely unknown. In this study, we conduct a literature survey on hysteresis caused by charge traps, analyzing the driving principle of organic transistors, different types of hysteresis, charge trap characteristics, estimation models, and their dynamics, as well as using causal inference methods for quantitative analysis of the relationship between hysteresis and traps.
Hysteresis in organic field-effect transistors is attributed to the well-known bias stress effects. This is a phenomenon in which the measured drain-source current varies when sweeping the gate voltage from on to off or from off to on. Hysteresis is caused by various factors, and one of the most common is charge trapping. A charge trap is a defect that occurs in an interface state or part of a semiconductor, and it refers to an electronic state that appears distributed in the semiconductor's energy band gap. Extensive research has been conducted recently on obtaining a better understanding of charge traps for hysteresis. However, it is still difficult to accurately measure or characterize them, and their effects on the hysteresis of organic transistors remain largely unknown. In this study, we conduct a literature survey on the hysteresis caused by charge traps from various perspectives. We first analyze the driving principle of organic transistors and introduce various types of hysteresis. Subsequently, we analyze charge traps and determine their influence on hysteresis. In particular, we analyze various estimation models for the traps and the dynamics of the hysteresis generated through these traps. Lastly, we conclude this study by explaining the causal inference approach, which is a machine learning technique typically used for current data analysis, and its implementation for the quantitative analysis of the causal relationship between the hysteresis and the traps.

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