JOURNAL ARTICLE

The Geometrical Effect of Single Walled Carbon Nanotube Network Transistors on Low Frequency Noise Characteristics

Un Jeong KimJoon Pyo KilWanjun Park

Year: 2011 Journal:   Journal of Nanoscience and Nanotechnology Vol: 11 (7)Pages: 6080-6083   Publisher: American Scientific Publishers

Abstract

The noise characteristics of randomly networked single walled carbon nanotubes grown directly by plasma enhanced chemical vapor deposition with field effect transistor. Geometrical complexity due to the large number of tube-tube junctions in the nanotube network is expected to be one of the key factors for the noise power of 1/f dependence. We investigated low frequency noise as a function of channel length (2-10 microm) and found that increased with longer channel length. Percolational behaviors of nanotube network that differs from ordinary semiconducting and metallic materials can be characterized by a geometrical picture with electrical homo- and hetero-junctions. Fixed nanotube density provides a test conditions to evaluate the contributions of junctions as a noise center. Hooge's empirical law is applied to investigate the low frequency noise characteristics of single walled carbon nanotube random network transistors. The noise power shows the dependence of the transistor channel length. It is understood that nanotube/nanotube junctions act as a noise center. However, the differences induced by channel length in the noise power are observed as not so significant. We conclude that tolerance of low frequency noise is important property for SWNT networks as an electronic device application.

Keywords:
Materials science Carbon nanotube field-effect transistor Carbon nanotube Noise (video) Nanotube Infrasound Noise power Transistor Carbon nanotube quantum dot Field-effect transistor Chemical vapor deposition Optoelectronics Nanotechnology Power (physics) Voltage Acoustics Electrical engineering Physics Computer science

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Topics

Carbon Nanotubes in Composites
Physical Sciences →  Materials Science →  Materials Chemistry
Neural Networks and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Mechanical and Optical Resonators
Physical Sciences →  Physics and Astronomy →  Atomic and Molecular Physics, and Optics
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