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Atomistic Modeling of Complex Silicon Processing Scenarios

Published online by Cambridge University Press:  17 March 2011

Martin Jaraiz,
Pedro Castrillo,
Ruth Pinacho,
Lourdes Pelaz,
Juan Barbolla,
George H. Gilmer and
Conor S. Rafferty
Martin Jaraiz
Affiliation:
Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain
Pedro Castrillo
Affiliation:
Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain
Ruth Pinacho
Affiliation:
Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain
Lourdes Pelaz
Affiliation:
Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain
Juan Barbolla
Affiliation:
Dept. de Electronica, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain
George H. Gilmer
Affiliation:
Lucent Technologies Bell Labs, 600 Mountain Ave., Murray Hill, NJ 07974, U.S.A
Conor S. Rafferty
Affiliation:
Lucent Technologies Bell Labs, 600 Mountain Ave., Murray Hill, NJ 07974, U.S.A
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Abstract

The level of sophistication reached by today's Si device fabrication technologies has called for new modeling and simulation schemes, capable of handling the wide variety of interaction mechanisms that govern the complex phenomena that can occur at the atomic level. The kinetic Monte Carlo (KMC) technique seems particularly apt for this task. It takes as input basic materials parameters, derived from ab-initio calculations or from experiments, and is capable of carrying out a detailed simulation up to the dimensions and time scales of current ULSI Si device manufacture. In addition, it can accommodate and efficiently simulate complex interactions between multiple dopant and defect types. We explain the approach and show examples of application in both materials processing and device fabrication. Finally, we present the use of some artificial intelligence techniques (namely, genetic algorithms) that look most promising as methodologies that can easy and efficiently be employed to build the extensive KMC parameter database.

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References

1 Voter, A. F., Phys. Rev. Lett., 78, 3908 (1997).10.1103/PhysRevLett.78.3908Google Scholar
2 Jaraiz, M., Pelaz, L., Rubio, E., Barbolla, J., Gilmer, G. H., Eaglesham, D. J., Gossmann, H. J. and Poate, J. M., Mat. Res. Soc. Symp. Proc., 532, 43 (1998).10.1557/PROC-532-43Google Scholar
3 Pelaz, L., Gilmer, G. H., Venezia, V. C., Gossmann, H. J., Jaraiz, M. and Barbolla, J., Appl. Phys. Lett., 74, 2017 (1999).10.1063/1.123742Google Scholar
4 Packan, P. A. and Plummer, J. D., Appl. Phys. Lett., 56, 1787 (1990).10.1063/1.103100Google Scholar
5 Wong, H.-S., Taur, Y., Tech. Dig. Int. Electron Devices Meet., 705 (1993).Google Scholar
6 Asenov, A., Tech. Dig. Int. Electron Devices Meet., 223 (1998).Google Scholar
7 Fogel, D. B., IEEE Spectrum, p. 26, Feb. 2000.10.1109/6.819926Google Scholar
8 Deaven, D. M. and Ho, K. M., Phys. Rev. Lett., 75, 288 (1995).10.1103/PhysRevLett.75.288Google Scholar
9 Ho, K. M., Shvartsburg, A. A., Pan, B., Lu, Z. Y., Wang, C. Z., Wacker, J. G., Fye, J. L., Jarrold, M. F., Nature, 392, 582 (1998).10.1038/33369Google Scholar
10 Pinacho, R., et al. these Proceedings.Google Scholar
11 Man, K. F., Tang, K. S., Kwong, S., Genetic Algorithms, Springer, London 1999.10.1007/978-1-4471-0577-0Google Scholar
12Genetic Algorithm library from http://lancet.mit.edu/ga/.Google Scholar
13 Banzhaf, W., Nordin, P., Keller, R. E., Francone, F. D., Genetic Programming, Chapter 12, Morgan Kaufmann Publishers, S. Francisco CA, 1998.10.1007/BFb0055923Google Scholar