Abstract
This paper presents a compact analytical model for the organic field-effect transistors (OFETs), which describes two main aspects, the first one is related to the behavior in above threshold regime, while the other corresponds to the below threshold regime. The total drain current in the OFET device is calculated as the sum of two components, with the inclusion of a smooth transition function in order to take into account both regions using a single expression. A genetic algorithm based approach (GA) is investigated as a parameter extraction tool in the case of the compact OFET model to find the parameters’ values from experimental data such as: mobility enhancement factor γ, threshold voltage V Th , subthreshold swing S, channel length modulation λ, and knee region sharpness m. The comparison of the developed current model with the experimental data shows a good agreement in terms of the transfer and the output characteristics. Therefore, the GA based approach can be considered as a competitive candidate compared to the direct method.
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Recommended by Associate Editor Zi-Qiang Zhu
Imad Benacer received the B. Eng. and M. Sc. degrees from University of Batna, Algeria in 2006 and 2010, respectively. He is currently a Ph. D. degree candidate related to organic transistor.
His research interests include microelectronic devices, organic transistor, modeling, and artificial intelligence.
ORCID iD: 0000-0002-3063-9695
Zohir Dibi received the B. Sc. degree in electronics engineering from the University of Sitif, Algeria in 1994, and received the M. Eng. and Ph.D. degrees from the University of Constantine, Algeria, in 1998 and 2002, respectively. He has been the head of Electronics Department. He is currently an assistant professor in Electronics Department and vice-dean of the Faculty of Engineering at Batna University, Algeria.
His research interests include neural networks, sensors, smart sensors, and organic devices.
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Benacer, I., Dibi, Z. Extracting parameters of OFET before and after threshold voltage using genetic algorithms. Int. J. Autom. Comput. 13, 382–391 (2016). https://doi.org/10.1007/s11633-015-0918-6
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DOI: https://doi.org/10.1007/s11633-015-0918-6