Vadim A. Shakhnov
BMSTU
Vadim Shakhnov received the degree in Electrical Engineering in 1966 from the Bauman Moscow State Technical University, USSR. He joined the Bauman Moscow State Technical University as a Full Professor in 1991. He is an associated member of Russian Academy of Science since 2008. Now he is a Head of Department and a Chair of Nanoscale Engineering Council of the Bauman Moscow State Technical University. Professor Shakhnov is the author of 10 books and 200+ journal and conference papers. Professor Shakhnov's research interests include: - VLSI physical layout and circuit design; - Nanoscale Engineering; - Sensors; - Power Electronics.
In the paper, we present our approach to visual analytics support for research of carbon nanotube variation and its influence on thermal conduction phenomena. The outstanding thermal conductivity of carbon nanotubes attracts designers [1]. Carbon nanotubes FETs are promising candidates for the post silicon era. However, the fabrication of carbon nanotubes with the predefined properties is a big challenge [2]. Carbon nanotube specific variations, including nanotube diameter and chirality variations, influence the thermal conductivity of carbon nanotube [3]. A designer has to compare thermal properties of carbon nanotubes. However, carbon nanotubes are invisible for a human eye. Therefore, special efforts are required to find good design solutions [4].
In the paper, we focus on design solution with the predefined diameter. First, we find all possible chiral indices for the given diameter and then visualize all possible design solutions with their thermal properties. The corresponding statistical data is given as well.
We illustrate our approach for research of thermal properties of single-walled carbon nanotubes. We discuss our results for the zigzag and the armchair nanotubes.
Although nanotube devices are promising candidates for the coming post silicon era, more efforts for nanotube devices design automation are required. In the paper, we proposed a novel approach based on visual analytics. The approach supports a nanotube devices design process and simplifies a design solution choice.
This work was partially supported by grant RFBR 15-29-01115 ofi-m.
[1] G. Hills et al., "Rapid Co-Optimization of Processing and Circuit Design to Overcome Carbon Nanotube Variations," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 7, pp. 1082-1095, 2015.
[2] B. C. Paul et al., "Impact of a Process Variation on Nanowire and Nanotube Device Performance," IEEE Transactions on Electron Devices, 9, pp. 2369-2376, 2007.
[3] A. M. Marconnet, M. A. Panzer, and K. E. Goodson, "Thermal conduction phenomena in carbon nanotubes and related nanostructured materials", Rev. Mod. Phys. 85, no. 8, pp. 1296 -1327, 2013.
[4] V.A. Shakhnov et al., “Simulation and Visualization in Cognitive Nanoinformatics”, International Journal of Mathematics and Computers in Simulation, 1, pp. 141-147, 2014.
Nanoelectronic systems, components & devices , Carbon & graphene nanostructures
