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Atomistic Simulations of Effect of Coulombic Interactions on Carrier Fluctuations in Doped Silicon

Published online by Cambridge University Press:  01 February 2011

Zudian Qin
Affiliation:
University of Washington, Department of Electrical Engineering, Seattle WA 98195, U.S.A.
Scott T. Dunham
Affiliation:
University of Washington, Department of Electrical Engineering, Seattle WA 98195, U.S.A.
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Abstract

Carrier distributions associated with point charges in silicon solved with quantum perturbation theory are used to determine Coulombic interactions between charged defects in the presence of carrier screening. The resulting interactions are used in kinetic lattice Monte Carlo (KLMC) simulations of point defect-mediated diffusion to study dopant redistribution and associated variations in carrier concentration. Over a broad range of doping concentrations and temperatures, Coulombic repulsion between like dopants leads to ordering, resulting in a more uniform electrical potential distribution and therefore reduced variations in device performance compared with random doping, the standard condition assumed in previous doping fluctuation analyses.

Type
Research Article
Copyright
Copyright © Materials Research Society 2003

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References

1. Hoeneisen, B. and Mead, C. A., Solid-State Electron. 15, 819 (1972).Google Scholar
2. Keyes, R. W., Proc. IEEE 63, 740 (1975).Google Scholar
3. Mizuno, T., Okamura, J., and Toriumi, A., IEEE Trans. Electron Dev. 41, 2216 (1994).Google Scholar
4. Asenov, A., Slavcheva, G., Brown, A. R., Davies, J. H., and Saini, S., IEEE Trans. Electron Dev. 48, 722 (2001).Google Scholar
5. Sano, N. and Tomizawa, M., Appl. Phys. Lett. 79, 2267 (2001).Google Scholar
6. Mayergoyz, I. D. and Andrei, P., J. Appl. Phys. 90, 3019 (2001).Google Scholar
7. Yasuda, Y., Takamiya, M., and Hiramoto, T., IEEE Trans. Electron Dev. 47, 1838 (2000).Google Scholar
8. Tang, X., De, V. K., and Meindl, J. D., IEEE Trans. VLSI Systems 5, 369 (1997).Google Scholar
9. Dunham, S. T. and Wu, C. D., J. Appl. Phys. 78, 2362 (1995).Google Scholar
10. Bunea, M.M. and Dunham, S.T., in Semiconductor Process and Device Performance Modeling, MRS Proc. 490, edited by Dunham, S. T. and Nelson, J. (Pittsburgh, 1998), p. 38.Google Scholar
11. Qinand, Z. Dunham, S. T., MRS Proc. 717, C3.8 (2002).Google Scholar
12. International Technology Roadmap for Semiconductors (ITRS): Executive Summary (2001), p. 38, Table 1f.Google Scholar