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Modeling Metal Thin Film Growth under IPVD Conditions using Molecular Dynamics Rates in a Level Set Approach

Published online by Cambridge University Press:  10 February 2011

U. Hansen
Affiliation:
Walter Schottky Institute, Technical University of Munich, D-85748 Garching, Germany Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, USA
S. Rodgers
Affiliation:
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, USA
M. Nemirovskaya
Affiliation:
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, USA
K. F. Jensen
Affiliation:
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, USA
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Abstract

We present a recently developed method for modeling ionized physical vapor deposition. Using molecular dynamics techniques we examine the surface adsorption, reflection and sputter reactions taking place during ionized physical vapor deposition. We predict their relative probabilities and combine the information obtained from molecular dynamics into a transport model incorporating all effects of re-emission and re-sputtering. This provides a complete growth rate model that allows the inclusion of energy and angular dependent surface reaction rates. As an example, the method is applied to growth of an aluminum film under different deposition conditions.

Type
Research Article
Copyright
Copyright © Materials Research Society 2000

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References

1. Wang, W., Foster, J., Snodgrass, T., Wendt, A. E., and Booske, J. H., J. Appl. Phys. 85, 7556(1996).Google Scholar
2. Sheergar, M. K., Smy, T. D., Dew, S. K. and Brett, M. J., J. Vac. Sci. Technol. B 14, 2595(1996).Google Scholar
3. Cerio, F., Drewery, J., Huang, E. and Reynolds, G., J. Vac. Sci. Technol. A 16, 1863(1999).Google Scholar
4. Lum, C., Forster, J. E., Snodgrass, T. G., Booske, J. H., and Wendt, A. E., J. Vac. Sci. Technol. A 17, 840(1999).Google Scholar
5. Dabrowski, J., ussig, H.-J. M., Duane, M., Dunham, S. T., Goossens, R. and Vuong, H.-H., Advances in Solid State Physics 38, 595(1998).Google Scholar
6. Knorr, D. B., Merchant, S. M. and Biberger, M. A., J. Vac. Sci. Technol. B 16, 2734(1998).Google Scholar
7. Coronell, D. G., Hanson, D. E., Voter, A. F., Liu, C. L. and Kress, J. D., Appl. Phys. Lett. 73, 3860(1998).Google Scholar
8. Kress, J. D., Hanson, D. E., Voter, A. F., Liu, C. L., Liu, X. Y. and Coronell, D. G., submitted to J. Vac. Sci. Tech.Google Scholar
9. Hansen, U., Vogl, P. and Fiorentini, V., Phys. Rev. B 59, 7856(1999).Google Scholar
10. Frenkel, D. and Smit, B., Understanding Molecular Simulations: From Algorithms to Applications (Academic Press, Boston 1996).Google Scholar
11. Allen, M. P. and Tildesley, D. J., Computer Simulation of Liquids (Oxford UP, Oxford 1996).Google Scholar
12. Sethian, J. A., Level Set Methods and Fast Marching Methods (Cambridge UP, Cambridge 1999).Google Scholar
13. Hansen, U., Vogl, P. and Fiorentini, V., Phys. Rev. B 60, 5055(1999).Google Scholar
14. Hansen, U. and Kersch, A., Phys. Rev. B. 60, 14417(1999).Google Scholar
15. Stumpf, R. and Sche_er, M., Phys. Rev. B 53, 4958(1996).Google Scholar
16. Rossnagel, S. M., IBM J. Res. Dev. 43, 163(1999).Google Scholar
17. Dullini, E., Nucl. Instr. and Meth. B 2, 610(1984).Google Scholar
18. Kratzer, M., Brinkamnn, R. P., Schmidt, H. and Wachutka, G., submitted to J. Appl. Phys.Google Scholar