Numerical analysis of nonlinear model of excited carrier decay

Natalija Tumanova; Raimondas Čiegis; Mečislavas Meilūnas

Open Mathematics (2013)

  • Volume: 11, Issue: 6, page 1140-1152
  • ISSN: 2391-5455

Abstract

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This paper presents a mathematical model for photo-excited carrier decay in a semiconductor. Due to the carrier trapping states and recombination centers in the bandgap, the carrier decay process is defined by the system of nonlinear differential equations. The system of nonlinear ordinary differential equations is approximated by linearized backward Euler scheme. Some a priori estimates of the discrete solution are obtained and the convergence of the linearized backward Euler method is proved. The identifiability analysis of the parameters of deep centers is performed and the fitting of the model to experimental data is done by using the genetic optimization algorithm. Results of numerical experiments are presented.

How to cite

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Natalija Tumanova, Raimondas Čiegis, and Mečislavas Meilūnas. "Numerical analysis of nonlinear model of excited carrier decay." Open Mathematics 11.6 (2013): 1140-1152. <http://eudml.org/doc/269022>.

@article{NatalijaTumanova2013,
abstract = {This paper presents a mathematical model for photo-excited carrier decay in a semiconductor. Due to the carrier trapping states and recombination centers in the bandgap, the carrier decay process is defined by the system of nonlinear differential equations. The system of nonlinear ordinary differential equations is approximated by linearized backward Euler scheme. Some a priori estimates of the discrete solution are obtained and the convergence of the linearized backward Euler method is proved. The identifiability analysis of the parameters of deep centers is performed and the fitting of the model to experimental data is done by using the genetic optimization algorithm. Results of numerical experiments are presented.},
author = {Natalija Tumanova, Raimondas Čiegis, Mečislavas Meilūnas},
journal = {Open Mathematics},
keywords = {Nonlinear ODE; Numerical approximation; A priori estimates; Convergence; Model identification; nonlinear ODE; numerical approximation; a priori estimates; convergence; model identification},
language = {eng},
number = {6},
pages = {1140-1152},
title = {Numerical analysis of nonlinear model of excited carrier decay},
url = {http://eudml.org/doc/269022},
volume = {11},
year = {2013},
}

TY - JOUR
AU - Natalija Tumanova
AU - Raimondas Čiegis
AU - Mečislavas Meilūnas
TI - Numerical analysis of nonlinear model of excited carrier decay
JO - Open Mathematics
PY - 2013
VL - 11
IS - 6
SP - 1140
EP - 1152
AB - This paper presents a mathematical model for photo-excited carrier decay in a semiconductor. Due to the carrier trapping states and recombination centers in the bandgap, the carrier decay process is defined by the system of nonlinear differential equations. The system of nonlinear ordinary differential equations is approximated by linearized backward Euler scheme. Some a priori estimates of the discrete solution are obtained and the convergence of the linearized backward Euler method is proved. The identifiability analysis of the parameters of deep centers is performed and the fitting of the model to experimental data is done by using the genetic optimization algorithm. Results of numerical experiments are presented.
LA - eng
KW - Nonlinear ODE; Numerical approximation; A priori estimates; Convergence; Model identification; nonlinear ODE; numerical approximation; a priori estimates; convergence; model identification
UR - http://eudml.org/doc/269022
ER -

References

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  1. [1] Ascher U.M., Petzold L.R., Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, Society for Industrial and Applied Mathematics, Philadelphia, 1998 http://dx.doi.org/10.1137/1.9781611971392[Crossref] Zbl0908.65055
  2. [2] Carnevale N.T., Hines M.L., The NEURON Book, Cambridge University Press, Cambridge, 2006 http://dx.doi.org/10.1017/CBO9780511541612[Crossref] 
  3. [3] Chegis R.Yu., A study of difference schemes for a class of models of excitability, Comput. Math. Math. Phys., 1992, 32(6), 757–767 Zbl0772.65055
  4. [4] Chis O.-T., Banga J.R., Balsa-Canto E., Structural identifiability of systems biology models: a critical comparison of methods, PLoS ONE, 6(11), #e27755 
  5. [5] Čiegis R., Tumanova N., Finite-difference schemes for parabolic problems on graphs, Lith. Math. J., 2010, 50(2), 164–178 http://dx.doi.org/10.1007/s10986-010-9077-1[Crossref] Zbl1218.65081
  6. [6] Enns R.H., It’s a Nonlinear World, Springer Undergrad. Texts Math. Technol., Springer, New York, 2011 http://dx.doi.org/10.1007/978-0-387-75340-9[Crossref] 
  7. [7] Gerisch A., Chaplain M.A.J., Robust numerical methods for taxis-diffusion-reaction systems: applications to biomedical problems, Math. Comput. Modelling, 2006, 43(1-2), 49–75 http://dx.doi.org/10.1016/j.mcm.2004.05.016[Crossref] Zbl1086.92025
  8. [8] Goudon T., Miljanovic V., Schmeiser C., On the Shockley-Read-Hall Model: generation-recombination in semiconductors, SIAM J. Appl. Math., 2007, 67(4), 1183–1201 http://dx.doi.org/10.1137/060650751[WoS][Crossref] Zbl1146.82007
  9. [9] Hairer E., Nørsett S.P., Wanner G., Solving Ordinary Differential Equations I, Springer Ser. Comput. Math., 8, Springer, Berlin, 1993 Zbl0789.65048
  10. [10] Hairer E., Wanner G., Solving Ordinary Differential Equations II, Springer Ser. Comput. Math., 14, Springer, Berlin, 1996 http://dx.doi.org/10.1007/978-3-642-05221-7[Crossref] Zbl0859.65067
  11. [11] Hall R.N., Electron-hole recombination in Germanium, Physical Review, 1952, 87(2), 387–387 http://dx.doi.org/10.1103/PhysRev.87.387[Crossref] 
  12. [12] Hanslien M., Karlsen K.H., Tveito A., A maximum principle for an explicit finite difference scheme approximating the Hodgkin-Huxley model, BIT, 2005, 45(4), 725–741 http://dx.doi.org/10.1007/s10543-005-0023-2[Crossref] Zbl1099.65069
  13. [13] Hauser J.R., Numerical Methods for Nonlinear Engineering Models, Springer, Berlin, 2009 http://dx.doi.org/10.1007/978-1-4020-9920-5[Crossref] Zbl1173.65001
  14. [14] Hodgkin A., Huxley A., A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, 1952, 117(4), 500–544 
  15. [15] Horváth Z., Positivity of Runge-Kutta and diagonally split Runge-Kutta methods, Appl. Numer. Math., 1998, 28(2–4), 309–326 http://dx.doi.org/10.1016/S0168-9274(98)00050-6[Crossref] Zbl0926.65073
  16. [16] Hundsdorfer W., Verwer J.G., Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations, Springer Ser. Comput. Math., 33, Springer, Berlin-Heidelberg-New York-Tokyo, 2003 Zbl1030.65100
  17. [17] Ichimura M., Temperature dependence of a slow component of excess carrier decay curves, Solid-State Electronics, 2006, 50(11–12), 1761–1766 http://dx.doi.org/10.1016/j.sse.2006.10.001[Crossref] 
  18. [18] Macdonald D., Cuevas A., Validity of simplified Shockley-Read-Hall statistics for modeling carrier lifetimes in crystalline silicon, Phys. Rev. B, 2003, 67(7), #075203 http://dx.doi.org/10.1103/PhysRevB.67.075203[Crossref] 
  19. [19] Mascagni M., The backward Euler method for numerical solution of the Hodgkin-Huxley equations of nerve conduction, SIAM J. Numer. Anal., 1990, 27(4), 941–962 http://dx.doi.org/10.1137/0727054[Crossref] Zbl0707.92006
  20. [20] Miao H., Xia X., Perelson A.S., Wu H., On identifiability of nonlinear ODE models and applications in viral dynamics, SIAM Rev., 2011, 53(1), 3–39 http://dx.doi.org/10.1137/090757009[Crossref][WoS] Zbl1215.34015
  21. [21] Mitra S., Mitra A., Kundu D., Genetic algorithm and M-estimator based robust sequential estimation of parameters of nonlinear sinusoidal signals, Commun. Nonlinear Sci. Numer. Simul., 2011, 16(7), 2796–2809 http://dx.doi.org/10.1016/j.cnsns.2010.10.005[Crossref][WoS] Zbl1221.94020
  22. [22] Pincevičius A., Meilūnas M., Tumanova N., Numerical simulation of the conductivity relaxation in the high resistivity semiconductor, Math. Model. Anal., 2007, 12(3), 379–388 http://dx.doi.org/10.3846/1392-6292.2007.12.379-388[Crossref][WoS] Zbl1132.78312
  23. [23] Schmerler S., Hahn T., Hahn S., Niklas J., Gründig-Wendrock B., Explanation of positive and negative PICTS peaks in SI-GaAs, Journal of Materials Science: Materials in Electronics, 2008, 19(S1), 328–332 http://dx.doi.org/10.1007/s10854-007-9564-2[Crossref] 
  24. [24] Shockley W., Read W.T., Statistics of the recombinations of holes and electrons, Phys. Rev., 1952, 87(5), 835–842 http://dx.doi.org/10.1103/PhysRev.87.835[Crossref] Zbl0046.45106
  25. [25] Sivanandam S.N., Deepa S.N., Introduction to Genetic Algorithms, Springer, Berlin, 2010 
  26. [26] Starikovičius V., Čiegis R., Iliev O., A parallel solver for the design of oil filters, Math. Model. Anal., 2011, 16(2), 326–341 http://dx.doi.org/10.3846/13926292.2011.582591[Crossref][WoS] Zbl1301.76076
  27. [27] Sundnes J., Lines G.T., Cai X., Nielsen B.F., Mardal K.-A., Tveito A., Computing the Electrical Activity in the Heart, Monogr. Comput. Sci. Eng., 1, Springer, Berlin, 2006 Zbl1182.92020
  28. [28] Tikidji-Hamburyan R.A., Genetic algorithm modification to speed up parameter fitting for a multicompartment neuron model, BMC Neuroscience, 2008, 9(S1), #P90 [Crossref] 
  29. [29] Tumanova N., Čiegis R., Predictor-corrector domain decomposition algorithm for parabolic problems on graphs, Math. Model. Anal., 2012, 17(1), 113–127 http://dx.doi.org/10.3846/13926292.2012.645891[WoS][Crossref] Zbl1246.65156
  30. [30] Van Geit W., De Schutter E., Achard P., Automated neuron model optimization techniques: a review, Biol. Cybernet., 2008, 99(4–5), 241–251 http://dx.doi.org/10.1007/s00422-008-0257-6[Crossref] Zbl1154.92013
  31. [31] Yao L., Sethares W.A., Nonlinear parameter estimation via the genetic algorithm, IEEE Trans. Signal Process., 1994, 42(4), 927–935 http://dx.doi.org/10.1109/78.285655[Crossref] 

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