Extracting the field-effect mobilities of random semiconducting single-walled carbon nanotube networks: a critical comparison of methods

The field-effect mobility is an important figure of merit for semiconductors such as random networks of single-walled carbon nanotubes (SWNTs). However, owing to their network properties and quantum capacitance, the standard models for field-effect transistors cannot be applied without modifications...

Full description

Saved in:
Bibliographic Details
Main Authors: Schießl, Stefan Patrick (Author) , Rother, Marcel (Author) , Lüttgens, Jan (Author) , Zaumseil, Jana (Author)
Format: Article (Journal)
Language:English
Published: 7 November 2017
In: Applied physics letters
Year: 2017, Volume: 111, Issue: 19
ISSN:1077-3118
DOI:10.1063/1.5006877
Online Access:Verlag, Volltext: http://dx.doi.org/10.1063/1.5006877
Verlag, Volltext: https://aip.scitation.org/doi/full/10.1063/1.5006877
Get full text
Author Notes:Stefan P. Schießl, Marcel Rother, Jan Lüttgens and Jana Zaumseil
Description
Summary:The field-effect mobility is an important figure of merit for semiconductors such as random networks of single-walled carbon nanotubes (SWNTs). However, owing to their network properties and quantum capacitance, the standard models for field-effect transistors cannot be applied without modifications. Several different methods are used to determine the mobility with often very different results. We fabricated and characterized field-effect transistors with different polymer-sorted, semiconducting SWNT network densities ranging from low (≈6 μm−1) to densely packed quasi-monolayers (≈26 μm−1) with a maximum on-conductance of 0.24 μS μm−1 and compared four different techniques to evaluate the field-effect mobility. We demonstrate the limits and requirements for each method with regard to device layout and carrier accumulation. We find that techniques that take into account the measured capacitance on the active device give the most reliable mobility values. Finally, we compare our experimental results to a random-resistor-network model.
Item Description:Gesehen am 04.05.2018
Physical Description:Online Resource
ISSN:1077-3118
DOI:10.1063/1.5006877