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Theory and Practice of "Striping" for Improved ON/OFF Ratio in Carbon Nanonet Thin Film Transistors
),
Muhammad A. Alam1(
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A new technique to reduce the influence of metallic carbon nanotubes (CNTs)—relevant for large-scale integrated circuits based on CNT-nanonet transistors—is proposed and verified. Historically, electrical and chemical filtering of the metallic CNTs have been used to improve the ON/OFF ratio of CNT-nanonet transistors; however, the corresponding degradation in ON-current has made these techniques somewhat unsatisfactory. Here, we abandon the classical approaches in favor of a new approach based on relocation of asymmetric percolation threshold of CNT-nanonet transistors by a technique called "striping"; this allows fabrication of transistors with ON/OFF ratio > 1000 and ON-current degradation no more than a factor of 2. We offer first principle numerical models, experimental confirmation, and renormalization arguments to provide a broad theoretical and experimental foundation of the proposed method.
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Theory and Practice of "Striping" for Improved ON/OFF Ratio in Carbon Nanonet Thin Film Transistors
), Muhammad A. Alam1(
)
Abstract
A new technique to reduce the influence of metallic carbon nanotubes (CNTs)—relevant for large-scale integrated circuits based on CNT-nanonet transistors—is proposed and verified. Historically, electrical and chemical filtering of the metallic CNTs have been used to improve the ON/OFF ratio of CNT-nanonet transistors; however, the corresponding degradation in ON-current has made these techniques somewhat unsatisfactory. Here, we abandon the classical approaches in favor of a new approach based on relocation of asymmetric percolation threshold of CNT-nanonet transistors by a technique called "striping"; this allows fabrication of transistors with ON/OFF ratio > 1000 and ON-current degradation no more than a factor of 2. We offer first principle numerical models, experimental confirmation, and renormalization arguments to provide a broad theoretical and experimental foundation of the proposed method.
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Acknowledgements
N. Pimpartat. and M. A. Alam would like to thank S. Kumar and J. Murthy for help with generating random networks and the Network for Computational Nanotechnology and the Lilly Foundation for financial support. Q. Cao. and J. R. Rogers would like to thank T. Banks for help with processing. We thank the National Science Foundation (NIRT-0403489), the Department of Energy (DE-FG02-07ER46471), Motorola, Inc., the Frederick-Seitz Materials Research Laboratory, and the Center for Microanalysis of Materials (DE-FG02-07ER46453 and DE-FG02-07ER46471) at the University of Illinois.
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