Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-27T02:14:38.521Z Has data issue: false hasContentIssue false
  • English
  • Français

Rapid adatom island decay on Cu(111): a kinetic Monte Carlo simulation study

Published online by Cambridge University Press:  21 March 2011

Mats I. Larsson
Mats I. Larsson*
Affiliation:
Department of Physics, Universitetsgatan 1, Karlstad University, SE-65188 Karlstad, Sweden
Get access

Abstract

Kinetic Monte Carlo (KMC) simulations are used to investigate the recent scanning tunneling microscopy (STM) measurements of fast decaying adatom islands on Cu(111). The KMC model is a full diffusion bond-counting model including nearest neighbor as well as second-nearest neighbor interactions. For encounters between steps in adjacent atomic layers of an island it is demonstrated that a moderately reduced activation energy for interlayer adatom transport is enough to obtain correspondence between simulations and experiments, provided that the one-dimensional Ehrlich-Schwoebel barrier for corner transitions is reduced to zero. The results presented in this report are interesting because they demonstrate that step-edge crossing by simple adatom hopping is sufficient to explain the rapid island-decay mechanism.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 672: Symposium O – Mechanisms of Surface and Microstructure Evolution in Deposited Films and Film Structures , 2001 , O1.3
Copyright
Copyright © Materials Research Society 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Giesen, M., Icking-Konert, G. Schulze, and Ibach, H., Phys. Rev. Lett. 80, 552 (1998).Google Scholar
2. Icking-Konert, G. Schulze, Giesen, M., and Ibach, H., Surf. Sci. 398, 37 (1998).Google Scholar
3. Morgenstern, K., Rosenfeld, G., Comsa, G., Lægsgaard, E., and Besenbacher, F., Phys. Rev. Lett. 85, 468 (2000).Google Scholar
4. Morgenstern, K., Rosenfeld, G., Comsa, G., Sørensen, M. R., Hammer, B., Lægsgaard, E., and Besenbacher, F., Phys. Rev. B 63, 045412 (2001).Google Scholar
5. Giesen, M., Icking-Konert, G. Schultze, and Ibach, H., Phys. Rev. Lett. 82, 3101 (1999).Google Scholar
6. Giesen, M. and Ibach, H., Surf. Sci. 464, L697 (2000).Google Scholar
7. Larsson, M. I., Phys. Rev. B 56, 15157 (1997).Google Scholar
8. Monte Carlo Simulation in Statistical Physics An Introduction, Binder, K. and Heermann, D. W., Springer Verlag, Berlin, 1997.Google Scholar
9. Breeman, M., Barkema, G. T., and Boerma, D. O., Surf. Sci. 323, 71 (1995).Google Scholar
10. Papanicolaou, N.I and Evangelakis, G. A., in Surface diffusion atomistic and collective processes, ed. Tringides, M. C., NATO ASI Series B 360, Plenum Press, New York, 1997.Google Scholar
11. Giesen, M. and Ibach, H., Surf. Sci. 431, 109 (1999).Google Scholar
12. Maksym, P. A., Semicond. Sci. Technol. 3, 594 (1988).Google Scholar
13. Vvedensky, D. D. and Clarke, S., Surf. Sci. 225, 373 (1990).Google Scholar
14. Bogicevic, A., Strömquist, J., and Lundqvist, B. I., Phys. Rev. Lett. 81, 637 (1998)Google Scholar
15. Li, Y. and DePristo, A. E., Surf. Sci. 351, 189 (1996).Google Scholar
16. Feibelman, P. J., Surf. Sci., in press.Google Scholar
17. Evangelakis, G. A., Vamvakopoulos, E., Pantelios, D., and Papanicolaou, N. I., Surf. Sci. 425, L393 (1999).Google Scholar
18. Karimi, M., Tomkowski, T., Vidali, G., and Biham, O., Phys. Rev. B 52, 5364 (1995).Google Scholar
19. Stoltze, P., J. Phys.: Condens. Matter 6, 9495 (1994).Google Scholar