Modeling SWCNT Bandgap and Effective Mass Variation Using a Monte Carlo Approach

被引:53
|
作者
El Shabrawy, Karim [1 ]
Maharatna, Koushik [1 ]
Bagnall, Darren [1 ]
Al-Hashimi, Bashir M. [1 ]
机构
[1] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England
关键词
Bandgap variation; carbon-nanotube (CNT) device models; effective mass variation; single-walled CNT (SWCNT); third-nearest-neighbor tight-binding (TB) model; WALLED CARBON NANOTUBES; ELECTRONIC-STRUCTURE; DIAMETER; TRANSISTORS; GROWTH; TEMPERATURE; PRESSURE; MONOXIDE; SCIENCE;
D O I
10.1109/TNANO.2009.2028343
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthesizing single-walled carbon nanotubes (SWCNTs) with accurate structural control has been widely acknowledged as an exceedingly complex task culminating in the realization of CNT devices with uncertain electronic behavior. In this paper, we apply a statistical approach in predicting the SWCNT bandgap and effective mass variation for typical uncertainties associated with the geometrical structure. This is first carried out by proposing a simulation-efficient analytical model that evaluates the bandgap (Eg) of an isolated SWCNT with a specified diameter (d) and chirality (theta). Similarly, we develop an SWCNT effective mass model, which is applicable to CNTs of any chirality and diameters >1 nm. A Monte Carlo method is later adopted to simulate the bandgap and effective mass variation for a selection of structural parameter distributions. As a result, we establish analytical expressions that separately specify the bandgap and effective mass variability (Eg(sigma), m(sigma)*) with respect to the CNT mean diameter (d(mu)) and standard deviation (d(sigma)). These expressions offer insight from a theoretical perspective on the optimization of diameter-related process parameters with the aim of suppressing bandgap and effective mass variation.
引用
收藏
页码:184 / 193
页数:10
相关论文
共 50 条
  • [31] CAUTION IN USING MONTE-CARLO KINETICS MODELING
    SOLTZBERG, LJ
    WABER, FG
    JOURNAL OF CHEMICAL EDUCATION, 1974, 51 (09) : 576 - 576
  • [32] Modeling Interference Using Monte Carlo Ray Trace
    Cassarly, Bill J.
    Lin, Alexander
    ILLUMINATION OPTICS VI, 2021, 11874
  • [33] Retention modeling of nanocrystalline flash memories: A Monte Carlo approach
    Ghosh, Bahniman
    Liu, Hai
    Winstead, Brian
    Foisy, Mark C.
    Banerjee, Sanjay K.
    SOLID-STATE ELECTRONICS, 2010, 54 (11) : 1295 - 1299
  • [34] A microscopic Monte Carlo approach to modeling of Resistive Plate Chambers
    Bosnjakovic, D.
    Petrovic, Z. Lj.
    Dujko, S.
    JOURNAL OF INSTRUMENTATION, 2014, 9
  • [35] Modeling of polystyrene degradation using kinetic Monte Carlo
    da Mata Costa, Laura Pires
    Brandao, Amanda L. T.
    Pinto, Jose Carlos
    JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 2022, 167
  • [36] Modeling sub-90nm on-chip variation using Monte Carlo method for DFM
    Huang, Jun-Fu
    Chang, Victor C. Y.
    Liu, Sally
    Doong, Kelvin Y. Y.
    Chang, Keh-Jeng
    PROCEEDINGS OF THE ASP-DAC 2007, 2007, : 221 - +
  • [37] Implementation of surface roughness scattering in Monte Carlo modeling of thin SOI MOSFETs using the effective potential
    Ramey, SM
    Ferry, DK
    IEEE TRANSACTIONS ON NANOTECHNOLOGY, 2003, 2 (02) : 110 - 114
  • [38] Decomposition of electron ionization mass spectra for space application using a Monte-Carlo approach
    Gautier, Thomas
    Serigano, Joseph
    Bourgalais, Jeremy
    Horst, Sarah M.
    Trainer, Melissa G.
    RAPID COMMUNICATIONS IN MASS SPECTROMETRY, 2020, 34 (08)
  • [39] Prediction of casted muck pile profiles using discrete element modeling and the Monte Carlo approach
    Lamont, R.
    Schafrik, S.
    Diddle, B.
    Silva, J.
    Calnan, J.
    Agioutantis, Z.
    SIMULATION MODELLING PRACTICE AND THEORY, 2025, 140
  • [40] Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach
    Zhou, G
    Esaki, T
    Mitani, Y
    Xie, M
    Mori, J
    ENGINEERING GEOLOGY, 2003, 68 (3-4) : 373 - 386