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Kinetic Monte Carlo Simulation of Metallic Nanoislands Grown by Physical Vapor Deposition

Published online by Cambridge University Press:  20 August 2015

Abuhanif K. Bhuiyan ,
S. K. Dew  and
M. Stepanova
Abuhanif K. Bhuiyan*
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4 National Institute for Nanotechnology NRC, Edmonton, Alberta, Canada T6G 2M9
S. K. Dew*
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4
M. Stepanova*
Affiliation:
National Institute for Nanotechnology NRC, Edmonton, Alberta, Canada T6G 2M9
*
Email:[email protected]
Email:[email protected]
Corresponding author.Email:[email protected]
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Abstract

We report kinetic Monte-Karlo (KMC) simulation of self-assembled synthesis of nanocrystals by physical vapor deposition (PVD), which is one of most flexible, efficient, and clean techniques to fabricate nanopatterns. In particular, self-assembled arrays of nanocrystals can be synthesized by PVD. However size, shape and density of self-assembled nanocrystals are highly sensitive to the process conditions such as duration of deposition, temperature, substrate material, etc. To efficiently synthesize nanocrystalline arrays by PVD, the process control factors should be understood in detail. KMC simulations of film deposition are an important tool for understanding the mechanisms of film deposition. In this paper, we report a KMC modeling that explicitly represents PVD synthesis of self-assembled nanocrystals. We study how varying critical process parameters such as deposition rate, duration, temperature, and substrate type affect the lateral 2D morphologies of self-assembled metallic islands on substrates, and compare our results with experimentally observed surface morphologies generated by PVD. Our simulations align well with experimental results reported in the literature.

Keywords

82B2482B2682D2082D2582D80Kinetic Monte Carlo Simulation (KMC)nanocrystalsnanotechnology
Type
Research Article
Information
Communications in Computational Physics , Volume 9 , Issue 1 , January 2011 , pp. 49 - 67
Copyright
Copyright © Global Science Press Limited 2011

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References

[1]Reichelt, K., Nucleation and growth of thin films, Vacuum, 38 (1988) 1083.Google Scholar
[2]Amar, J.G. and Family, F., Kinetics of submonolayer and multilayer epitaxial growth, Thin Solid Films 272 (1996) 208.Google Scholar
[3]Pomeroy, J.M. and Brock, J.D., Critical nucleus phase diagram for the Cu (100) surface, Phys. Rev. B 73, (2006) 245405.Google Scholar
[4]Venables, J.A., Spiller, G.D.T. and Hanbucken, M., Nucleation and growth of thin films, Rep. Prog. Phys, 47, (1984) 399.CrossRefGoogle Scholar
[5]Venables, J.A., Nucleation and growth processes in thin film formation, J. Vac. Sci. Technol. B, 4 (1986) 870.Google Scholar
[6]Venables, J.A., Atomic processes in crystal growth, Surf. Sci. 299(1994) 798.Google Scholar
[7]Ratsch, C. and Venables, J.A., Nucleation theory and the early stages of thin film growth, J. Vac. Sci. Technol. A. 21, (2003) S96.Google Scholar
[8]Brune, H., Bales, G.S., Jacobsen, J., Boragno, C., and Kern, K., Measuring surface diffusion from nucleation island densities, Phys. Rev. B 60 (1999) 5991.Google Scholar
[9]Shirakawa, H. and Komiyama, H., Migration-coalescence of Nanoparticles During Deposition of Au, Ag, Cu, and GaAs on Amorphous SiO2, J. of Nanoparticle Research 1 (1999) 17.Google Scholar
[10]Frank, S., Wedler, H., Behm, R.J., Rottler, J., Maass, P., Approaching the low-temperature limit in nucleation and two-dimensional growth of fcc (100) metal films Ag/Ag(100), Phys. Rev. B 66 (2002) 155435.Google Scholar
[11]Zhang, C.M., Bartelt, M.C., Wen, J.M., Jenks, C.J., Evans, J.W. and Thiel, P.A., Submonolayer island formation and the onset of multilayer growth during Ag/Ag(100) homoepitaxy, Surf. Sci. 406 (1998) 178.Google Scholar
[12]Stroscio, J.A., Pierce, D.T., and Dragoset, R.A., Homoepitaxial growth of iron and real space view of Refection-High-Energy-Electron diffraction, Phys Rev. Lett. 70-23 (1993) 3615.Google Scholar
[13]Bruschi, P., Cagnoni, P., and Nannini, A., Temperature-dependent Monte Carlo simulations of thin metal film growth and percolation, Phys. Rev. B 55, (1997) 7955.Google Scholar
[14]Heino, P., Hakkinen, H., and Kaski, K., Molecular-dynamics study of copper with defects under strain, Phys. Rev. Bx 58-2, (1998) 641.Google Scholar
[15]Wolf, D., Yamakov, V., Phillpot, S.R., Mukherjee, A., and Gleiter, H., Deformation of Nanocrys-talline Materials by Molecular-Dynamics Simulation: Relationship toExperiments, Acta Materialia 53 (2005) 1.CrossRefGoogle Scholar
[16]Erkoc, S., Stability of Gold Clusters: Molecular-Dynamics Simulations, Physica E, 8 (2000) 210.CrossRefGoogle Scholar
[17]Huang, H., Gilmer, G.H. and de La Rubia, T.D., Anatomistic simulator for thin film deposition in three dimensions, J. Appl. Phys. 84 (1998) 3636.Google Scholar
[18]Frank, S., and Rikvold, P. A., Kinetic Monte Carlo simulations of electrodeposition: Crossover from continuous to instantaneous homogeneous nucleation within Avrami’s law, Surf. Sci. 600 (2006) 2470.Google Scholar
[19]Wang, L.G. and Clancy, P., Kinetic Monte Carlo simulation of the growth of polycrystalline Cu film, Surf. Sci. 473 (2001), 25.Google Scholar
[20]Abraham, F.F. and White, G.M., Simulation Time Versus Real Time in Computer Simulation of Vapor Deposition, J. Appl. Phys. 41 (1970) 184.Google Scholar
[21]Wei, H.L., Liu, Z.L. and Yao, K.L., Influence of microstructure of substrate surface on early stage of thin film growth, Vacuum 56 (2000), 185.Google Scholar
[22]Nurminen, L., Kuronen, A. and Kaski, K., Kinetic Monte Carlo simulation of nucleation on patterned substrates, Phys. Rev. B 63 1-7 (2000), 035407.Google Scholar
[23]Landau, D.P., Pal, S. and Shim, Y., Monte Carlo Simulations of Film Growth, Comput. Phys. Comm. 121 (1999) 341.Google Scholar
[24]Biham, O., Furman, I., Karimi, M., Kennett, R., Vidali, G. and Zeng, H., Models for diffusion and island growth in metal monolayers, Surf. Sci. 400 (1998), 29.Google Scholar
[25]Hildebrand, M. and Mikhailov, A. S., Mesoscopic modeling in the theory of reactive adsor-bates, J. Phys. Chem. 100 (1996) 19089.Google Scholar
[26]Friedrich, L.J., Dew, S.K., Brett, M., and Smy, T., Thin film microstructure modelling through line-segment simulation, Thin Solid Films 266 (1995) 83.Google Scholar
[27]Evans, J.W., Thiel, P.A. and Bartelt, M.C., Morphological Evolution during Epitaxial Thin Film Growth: Formation of 2D Islands and 3D Mounds,” Surface Science Reports, Surface Science Reports 61 (2006) 1.Google Scholar
[28] In this work, the interatomic distances for Cu and Ag were taken similar in order to facilitate comparison of the kinetic factors. The slight (10Google Scholar
[29]Kambe, K., Cohesive Energy of Noble Metals, Physical Review, 99 (1955) 419.Google Scholar
[30] In Model 1, an atom has 8 lateral neighbors and 9 neighbors below on the substrate, so the total number of neighbor atoms is 17. Model 2 adopts 4 lateral neighbors and 5 effective bonds with the substrate. Thus, our estimate of lateral bonding based on the measured cohesive energy for Cu provides the energy per bond of approximately 0.20 eV for Model 1, and 0.39 eV for Model 2. For Ag the values are 0.17 and 0.32 eV, respectively.Google Scholar
[31]Uppenbrink, J. and Wales, D. J., Structure and energetics of model metal clusters, J. Chem Phys, 96 (1992) 8520.Google Scholar
[32]Eckstein, W., Computer simulation of ion-solid interactions, Springer-Verlag, Berlin, 1991Google Scholar
[33] The description of the SRIM code can be found at http://www.srim.org/.Google Scholar