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
  • [1] SIMOCF: Modeling fractional crystallization using a Monte Carlo approach
    Verma, SP
    Ciriaco-Villanueva, R
    Torres-Alvarado, IS
    COMPUTERS & GEOSCIENCES, 1998, 24 (10) : 1021 - 1027
  • [3] Modeling of layering growth processes using a Monte Carlo approach
    Rieck, Christian
    Bueck, Andreas
    Tsotsas, Evangelos
    IFAC PAPERSONLINE, 2015, 48 (01): : 99 - 104
  • [4] MONTE-CARLO EVALUATION OF THE EFFECTIVE GLUON MASS
    BERNARD, C
    PHYSICS LETTERS B, 1982, 108 (06) : 431 - 434
  • [5] Polaron effective mass from Monte Carlo simulations
    Kornilovitch, PE
    Pike, ER
    PHYSICAL REVIEW B, 1997, 55 (14) : R8634 - R8637
  • [6] Monte Carlo approach to Bayesian regression modeling
    Smid, J
    Volf, P
    Rao, G
    COMPUTER-INTENSIVE METHODS IN CONTROL AND SIGNAL PROCESSING: THE CURSE OF DIMENSIONALITY, 1997, : 169 - 180
  • [7] Kinetic Monte Carlo Modeling of Low-Bandgap Polymer Solar Cells
    Albes, Tim
    Popescu, Bogdan
    Popescu, Dan
    Loch, Marius
    Arca, Francesco
    Lugli, Paolo
    2014 IEEE 40TH PHOTOVOLTAIC SPECIALIST CONFERENCE (PVSC), 2014, : 57 - 62
  • [8] Modeling the Migration of Platinum Nanoparticles on Surfaces Using a Kinetic Monte Carlo Approach
    Li, Lin
    Plessow, Philipp N.
    Rieger, Michael
    Sauer, Simeon
    Sanchez-Carrera, Roel S.
    Schaefer, Ansgar
    Abild-Pedersen, Frank
    JOURNAL OF PHYSICAL CHEMISTRY C, 2017, 121 (08): : 4261 - 4269
  • [9] Bayesian connective field modeling using a Markov Chain Monte Carlo approach
    Invernizzi, Azzurra
    Haak, Koen V.
    Carvalho, Joana C.
    Renken, Remco J.
    Cornelissen, Frans W.
    NEUROIMAGE, 2022, 264
  • [10] Modeling of a neutron irradiator using Monte Carlo
    Santos, Raphael F. G.
    Rebello, Wilson F.
    Estrada, Julio J. S.
    Medeiros, Marcos P. C.
    Silva, Ademir X.
    Souza, Edmilson M.
    Costa, Rogerio F.
    Barbosa, Caroline M.
    Braga, Kelmo L.
    Braz, Delson
    APPLIED RADIATION AND ISOTOPES, 2020, 165 (165)