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Numerical comparison of three stochastic methods for nonlinear PN junction problems

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Abstract

We apply the Monte Carlo, stochastic Galerkin, and stochastic collocation methods to solving the drift-diffusion equations coupled with the Poisson equation arising in semiconductor devices with random rough surfaces. Instead of dividing the rough surface into slices, we use stochastic mapping to transform the original deterministic equations in a random domain into stochastic equations in the corresponding deterministic domain. A finite element discretization with the help of AFEPack is applied to the physical space, and the equations obtained are solved by the approximate Newton iterative method. Comparison of the three stochastic methods through numerical experiment on different PN junctions are given. The numerical results show that, for such a complicated nonlinear problem, the stochastic Galerkin method has no obvious advantages on efficiency except accuracy over the other two methods, and the stochastic collocation method combines the accuracy of the stochastic Galerkin method and the easy implementation of the Monte Carlo method.

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References

  1. Askey R, Wilson J. Some Basic Hypergeometric Orthogonal Polynomials that Generalize Jacobi Polynomials. Mem Amer Math Soc, No 319. Providence: Amer Math Soc, 1985

    Google Scholar 

  2. Babuška I, Tempone R, Zouraris G E. Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J Numer Anal, 2004, 42(2): 800–825

    Article  MATH  MathSciNet  Google Scholar 

  3. Bäck, J, Nobile F, Tamellini L, Tempone R. Stochastic spectral Galerkin and collocation methods for PDEs with random coefficients: a numerical comparison. In: Proceedings of the International Conference on Spectral and High Order Methods (ICOSAHOM 09). Berlin: Springer-Verlag, 2010

    Google Scholar 

  4. Bank R E, Rose D J. Global approximate Newton methods. Numer Math, 1981, 37: 279–295

    Article  MATH  MathSciNet  Google Scholar 

  5. Bank R E, Rose D J, Fichtner W. Numerical methods for semiconductor device simulation. SIAM J Sci Stat Comput, 1983, 4: 416–435

    Article  MATH  MathSciNet  Google Scholar 

  6. Bejan A. Shape and Structure, from Engineering to Nature. New York: Cambridge Univ Press, 2000

    MATH  Google Scholar 

  7. Bürgler J F, Bank R E, Fichtner W, Smith R K. A new discretization for the semiconductor current continuity equations. IEEE Trans Comput-Aided Design Integrat Circuits Sys, 1989, 8: 479–489

    Article  Google Scholar 

  8. Bürgler J F, Conghran W M, Fichtner Jr W. An adaptive grid refinement strategy for the drift-diffusion equations. IEEE Trans Comput-Aided Design Integrat Circuits Sys, 1991, 10: 1251–1258

    Article  Google Scholar 

  9. Deb M K, Babuška I M, Oden J T. Solution of stochastic partial differential equations using Galerkin finite element techniques. Comput Meth Appl Mech Engrg, 2001, 190: 6359–6372

    Article  MATH  Google Scholar 

  10. Elman H C, Miller C W, Phipps E T, Tuminaro R S. Assessment of collocation and Galerkin approaches to linear diffusion equations with random data. Int J Uncertainty Quant, 2011, 1(1): 19–33

    Article  MATH  MathSciNet  Google Scholar 

  11. Fishman G. Monte Carlo: Concepts, Algorithms, and Applications. New York: Springer-Verlag, 1996

    Book  MATH  Google Scholar 

  12. Ganapathysubramanian B, Zabaras N. Sparse grid collocation methods for stochastic natural convection problems. J Comput Phys, 2007, 225(1): 652–685

    Article  MATH  MathSciNet  Google Scholar 

  13. Ghanem R G, Spanos P. Stochastic Finite Elements: a Spectral Approach. New York: Springer, 1991

    Book  MATH  Google Scholar 

  14. Li R. On multi-mesh h-adaptive methods. J Sci Comput, 2005, 24(3): 321–341

    Article  MATH  MathSciNet  Google Scholar 

  15. Loève M. Probability Theory. 4th ed. Berlin: Springer-Verlag, 1977

    MATH  Google Scholar 

  16. Novak E, Ritter K. High dimensional integration of smooth functions over cubes. Numer Math, 1996, 75: 79–97

    Article  MATH  MathSciNet  Google Scholar 

  17. Novak E, Ritter K. Simple cubature formulas with high polynomial exactness. Constructive Approx, 1999, 15: 499–522

    Article  MATH  MathSciNet  Google Scholar 

  18. Smolyak S A. Quadrature and interpolation formulas for tensor products of certain classes of functions. Dokl Akad Nauk SSSR, 1963, 148: 1042–1045 (in Russian); Soviet Math Dokl, 1963, 4: 240–243

    MATH  MathSciNet  Google Scholar 

  19. Taur Y, Ning H. Fundamentals of Modern VLSI Devices. 2nd ed. Cambridge: Cambridge Univ Press, 2009

    Google Scholar 

  20. Van Trees H L. Detection, Estimation, and Modulation Theory, Part I. New York: Wiley, 1968

    MATH  Google Scholar 

  21. Wasilkowski G, Wozniakowski H. Explicit cost bounds of algorithms for multivariate tensor product problems. J Complexity, 1995, 11: 1–56

    Article  MATH  MathSciNet  Google Scholar 

  22. Wiener N. The homogeneous chaos. Amer J Math, 1938, 60: 897–936

    Article  MathSciNet  Google Scholar 

  23. Xiu D, Hesthaven J. High-order collocation methods for differential equations with random inputs. SIAM J Sci Comput, 2005, 27: 1118–1139

    Article  MATH  MathSciNet  Google Scholar 

  24. Xiu D, Karniadakis G E. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput, 2002, 24(2): 619–644

    Article  MATH  MathSciNet  Google Scholar 

  25. Xiu D, Karniadakis G E. Modeling uncertainty in steady state diffusion problems via generalized polynomial chaos. Comput Methods Appl Math Engrg, 2002, 191: 4927–4948

    Article  MATH  MathSciNet  Google Scholar 

  26. Xiu D, Karniadakis G E. Modeling uncertainty in flow simulations via generalized polynomial chaos. J Comput Phys, 2003, 187: 137–167

    Article  MATH  MathSciNet  Google Scholar 

  27. Xiu D, Tartakovsky D M. Numerical methods for differential equations in random domain. SIAM J Sci Comput, 2006, 28: 1167–1185

    Article  MATH  MathSciNet  Google Scholar 

  28. Yu S, Zhao Y, Zeng L, Du G, Kang J, Han R, Liu X. Impact of line-edge roughness on double-gate Schottky-barrier filed-effect transistors. IEEE Trans Electron Devices, 2009, 56(6): 1211–1219

    Article  Google Scholar 

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Correspondence to Tiao Lu.

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Yao, W., Lu, T. Numerical comparison of three stochastic methods for nonlinear PN junction problems. Front. Math. China 9, 659–698 (2014). https://doi.org/10.1007/s11464-013-0327-5

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