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An Immunological Algorithm for Doping Profile Optimization in Semiconductors Design

  • Giovanni Stracquadanio
  • Concetta Drago
  • Vittorio Romano
  • Giuseppe Nicosia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6209)

Abstract

The doping profile optimization in semiconductor has been tackled as a constrained optimization problem coupled with a drift-diffusion model to simulate the physical phenomenon. In order to design high performance semiconductor devices, a new immunological algorithm, the Constrained Immunological Algorithm (cIA), has been introduced. The experimental results confirm that cIA clearly outperforms previous state-of-the-art algorithms in doping profile optimization.

Keywords

Feasible Region Constrain Optimization Problem Versus Bias Current Gain Total Current Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Anile, A.M., Cutello, V., Nicosia, G., Rascuna, R., Spinella, S.: Comparison among evolutionary algorithms and classical optimization methods for circuit design problems. In: IEEE Congress on Evolutionary Computation, Edinburgh, vol. 1, pp. 765–772. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  2. 2.
    Jüngel, A.: Quasi-hydrodynamic semiconductor equations. Birkhäuser, Basel (2001)zbMATHGoogle Scholar
  3. 3.
    Degond, P., Gallego, S., Méhats, F.: An entropic quantum drift-diffusion model for electron transport in resonant tunneling diodes. J. of Computational Physics 221(1), 226–249 (2007)zbMATHCrossRefGoogle Scholar
  4. 4.
    El Ayyadi, A., Jungel, A.: Semiconductor simulations using a coupled quantum drift-diffusion Schrodinger-Poisson model. SIAM J. on Applied Mathematics 66(2), 554–572 (2006)CrossRefGoogle Scholar
  5. 5.
    Burger, M., Pinnau, R.: Fast optimal design of semiconductor devices. SIAM J. on Applied Mathematics, 108–126 (2003)Google Scholar
  6. 6.
    Biondi, T., Ciccazzo, C., Cutello, V., D’Antona, S., Nicosia, G., Spinella, S.: Multi-objective evolutionary algorithms and pattern search methods for circuit design problems. J. of Universal Computer Science 12(4), 432–449 (2006)Google Scholar
  7. 7.
    Price, W.: A controlled random search procedure for global optimisation. The Computer J. 20(4), 367–370 (1977)zbMATHCrossRefGoogle Scholar
  8. 8.
    Jones, D., Perttunen, C., Stuckman, B.: Lipschitzian optimization without the Lipschitz constant. J. of Optimization Theory and Applications 79(1), 157–181 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Sinha, A., Tiwari, S., Deb, K.: A population-based, steady-state procedure for real-parameter optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 514–521 (2005)Google Scholar
  10. 10.
    Markowich, P.: The stationary semiconductor device equations. Springer, Heidelberg (1986)Google Scholar
  11. 11.
    Scharfetter, D., Gummel, H.: Large-signal analysis of a silicon read diode oscillator. IEEE Trans. Electron Devices 16(1), 64–77 (1969)CrossRefGoogle Scholar
  12. 12.
    Abdallah, N., Degond, P.: On a hierarchy of macroscopic models for semiconductors. J. of Mathematical Physics 37, 3306–3333 (1996)zbMATHCrossRefGoogle Scholar
  13. 13.
    Chen, X., Chen, L., Jian, H.: The Dirichlet problem of the quantum drift-diffusion model. Nonlinear Analysis 69(9), 3084–3092 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Nicosia, G., Rinaudo, S., Sciacca, E.: An evolutionary algorithm-based approach to robust analog circuit design using constrained multi-objective optimization. In: AI 2007, pp. 175–183. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Di Stefano, V., Drago, C.R., Milazzo, C.: Evolutionary Algorithm for Doping Profile Optimization in Semiconductor Design. In: SIMAI, vol. 2, pp. 367–370 (2006)Google Scholar
  16. 16.
    Rinaudo, S., Moschella, F., Muscato, O., Ahile, A.M.: Controlled random search parallel algorithm for global optimization with distributed processes on multivendor cpus. In: Arkeryd, L., et al. (eds.) Progress in Industrial Mathematics - ECMI (1998)Google Scholar
  17. 17.
    Romano, V.: 2D numerical simulation of the MEP energy-transport model with a finite dierence scheme. J. of Computational Physics 221(2), 439–468 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Martin, J., Simpson, T.: Use of Kriging models to approximate deterministic computer models. AIAA J. 43(4), 853–863 (2005)CrossRefGoogle Scholar
  19. 19.
    Wan, Z., Igusa, T.: Statistics of Nadaraya-Watson estimator errors in surrogate-based optimization. Optimization and Engineering 7(3), 385–397 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    McDonald, D., Grantham, W., Tabor, W., Murphy, M.: Global and local optimization using radial basis function response surface models. Applied Mathematical Modelling 31(10), 2095–2110 (2007)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Giovanni Stracquadanio
    • 1
  • Concetta Drago
    • 1
  • Vittorio Romano
    • 1
  • Giuseppe Nicosia
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

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