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SCIENCE CHINA Information Sciences, Volume 60, Issue 3: 032103(2017) https://doi.org/10.1007/s11432-014-0876-4

Structure descriptor for surface passivation in the simulation of atomistic models

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  • ReceivedMar 21, 2015
  • AcceptedJul 27, 2015
  • PublishedNov 18, 2016

Abstract

Surface passivation is an essential step for atomistic simulations. There can be many possible surface passivation results for a given device model, such as semiconductor devices that consist of Si, GaAs, or other materials because the bonding directions of the surface atoms may not be unique. Based on the structure analysis of the given model, a generation method with structure descriptor~(SDG) is proposed for surface passivation. Compared with other existing solutions, the SDG method not only provides trimmer results, but also reduces the torsion angle energy of the model, which is preferred in the simulation of atomistic models. The efficiency of this method was validated through test results from several applications.


Funded by

National Natural Science Foundation of China~(61272019)

Hong Kong Research Grant Council~(AOE/P-04/08)


Acknowledgment

Acknowledgments

This work was supported by Hong Kong Research Grant Council~(AOE/P-04/08) and National Natural Science Foundation of China~(Grant No. 61272019).


References

[1] Kim D M, Khondker A N, Ahmed S S, et al. Theory of conduction in polysilicon: drift-diffusion approach in crystalline-amorphous-crystalline semiconductor system-Part I: small signal theory. IEEE Trans Electron Dev, 1984, 31: 480-493 CrossRef Google Scholar

[2] Khondker A N, Kim D M, Ahmed S S, et al. Theory of conduction in polysilicon: drift-diffusion approach in crystalline-amorphous-crystalline semiconductor system-Part II: general $I$-$V$ theory. IEEE Trans Electron Dev, 1984, 31: 493-500 CrossRef Google Scholar

[3] Green M A. Intrinsic concentration, effective densities of states, and effective mass in silicon. J Appl Phys, 1990, 67: 2944-2954 CrossRef Google Scholar

[4] Vasileska D, Mamaluy D, Khan H R, et al. Semiconductor device modeling. J Comput Theor Nanosci, 2008, 5: 999-1030 Google Scholar

[5] Martinez A, Kalna K, Sushko P V, et al. Impact of body-thickness-dependent band structure on scaling of double-gate MOSFETs: a DFT/NEGF study. IEEE Trans Nanotechnol, 2009, 8: 159-166 CrossRef Google Scholar

[6] Zhang L N, Zahid F, Zhu Y, et al. First principles simulations of nanoscale silicon devices with uniaxial strain. IEEE Trans Electron Dev, 2013, 60: 3527-3533 CrossRef Google Scholar

[7] Velichko O I, Shaman Y P, Kovaliova A P. Simulation of hydrogen diffusion and boron passivation in crystalline silicon. Modell Simul Mater Sci Eng, 2014, 22: 035003-3533 CrossRef Google Scholar

[8] Vo T, Williamson A J, Galli G. First principles simulations of the structural and electronic properties of silicon nanowires. Phys Rev B, 2006, 74: 045116-3533 CrossRef Google Scholar

[9] Yelundur V. Understanding and implementation of hydeogen passivation of deffects in string ribbon silicon for high-efficiency, manufacturable, silicon solar cells. Dissertation for the Doctoral Degree. Georgia: Georgia Institute of Technology, 2003. Google Scholar

[10] Taylor R D, Jewsbury P J, Essex J W. A review of protein-small molecule docking methods. J Comput-Aid Mol Des, 2002, 16: 151-166 CrossRef Google Scholar

[11] Yam C Y, Peng J, Chen Q, et al. A multi-scale modeling of junctionless field-effect transistors. Appl Phys Lett, 2013, 103: 062109-166 CrossRef Google Scholar

[12] Northrup J. Structure of Si(100)H: dependence on the H chemical potential. Phys Rev B, 1992, 44: 1419-1422 Google Scholar

[13] Materials studio---visualization and statistics software. v4.3.0.0. Accelrys Software Inc., 2008. Google Scholar

[14] HyperChem---molecular modeling system. v8.0. Hypercube Inc., 2007. Google Scholar

[15] Zevenbergen I S, Martynov Y V, Rasmussen F B, et al. Magnetic resonance spectroscopy of hydrogen-passivated double donors in silicon. Mater Sci Eng B, 1996, 36: 138-141 CrossRef Google Scholar

[16] Ma D D D, Lee C S, Au F C K, et al. Small-Diameter silicon nanowire surfaces. Science, 2003, 299: 1874-1877 CrossRef Google Scholar

[17] Hansen U, Vogl P. Hydrogen passivation of silicon surfaces: a classical molecular-dynamics study. Phys Rev B, 1998, 57: 295-304 Google Scholar

[18] Daras P, Zarpalas D, Axenopoulos A, et al. Three-dimensional shape-structure comparison method for protein classification. Trans Comput Biol Bioinf, 2006, 3: 193-207 CrossRef Google Scholar

[19] Rappe A K, Casewit C J, Colwell K S, et al. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Amer Chem Soc, 1992, 114: 10024-10035 CrossRef Google Scholar

[20] Cornell W D, Cieplak P, Bayly C I, et al. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Amer Chem Soc, 1995, 117: 5179-5197 CrossRef Google Scholar

[21] van Duin A C T, Strachan A, Stewman S, et al. ReaxFFSiO reactive force field for silicon and silicon oxide systems. J Phys Chem A, 2003, 107: 3803-3811 Google Scholar

[22] Postma H, Teepen T, Yao Z, et al. Carbon nanotube single-electron transistors at room temperature. Science, 2001, 293: 75-79 Google Scholar

[23] Furuhashi M. Chiral vector determination of carbon nanotubes by observation of interference patterns near the end cap. Phys Rev Lett, 2008, 101: 185503-79 CrossRef Google Scholar

[24] Franklin A D, Luisier M, Han S J, et al. Sub-10nm carbon nanotube transistor. Nano Lett, 2012, 12: 758-762 CrossRef Google Scholar

[25] Peng L M, Zhang Z Y, Wang S, et al. Carbon based nanoelectronics: materials and devices (in Chinese). Sci Sin Tech, 2014, 44: 1071-1086 Google Scholar

[26] Wang W C, Lee G, Huang M, et al. First-principles study of GaAs(001)-$\beta$2($2\times4$) surface oxidation and passivation with H, Cl, S, F, and GaO. J Appl Phys, 2010, 107: 103720-1086 CrossRef Google Scholar

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