Device and circuit-level models for carbon nanotube and graphene nanoribbon transistors

Metal-oxide semiconductor field-effect transistor (MOSFET) scaling throughout the years has enabled us to pack million of MOS transistors on a single chip to keep in pace with Moore’s Law. After forty years of advances in integrated circuit (IC) technology, the scaling of silicon (Si) MOSFET has entered the nanometer dimension with the introduction of 90 nm high volume manufacturing in 2004. The latest technological advancement has led to a low power, high-density and high-speed generation of processor. Nevertheless, the scaling of the Si MOSFET below 22 nm may soon meet its’ fundamental physical limitations. This threshold makes the possible use of novel devices and structures such as carbon nanotube field-effect transistors (CNTFETs) and graphene nanoribbon field-effect transistors (GNRFETs) for future nanoelectronics. The investigation explores the potential of these amazing carbon structures that exceed MOSFET capabilities in term of speed, scalability and power consumption. The research findings demonstrate the potential integration of carbon based technology into existing ICs. In particular, a simulation program with integrated circuit emphasis (SPICE) model for CNTFET and GNRFET in digital logic applications is presented. The device performance of these circuit models and their design layout are then compared to 45 nm and 90 nm MOSFET for benchmarking. It is revealed through the investigation that CNT and GNR channels can overcome the limitations imposed by Si channel length scaling and associated short channel effects while consuming smaller channel area at higher current density.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2011

Author dc:creator

• Tan, Michael Loong Peng

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f680d7ab-39b9-40dc-8da0-938413713140/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

en

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.14063

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/245117