zbMATH — the first resource for mathematics

Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality. (English) Zbl 1414.90318
Summary: We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective technology computer aided design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both single objective optimization (in order to fully maximize devices efficiency) and multi-objective optimization (in order to balance efficiency and cost); (f) local, global and “glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based identifiability analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices.
90C29 Multi-objective and goal programming
90C90 Applications of mathematical programming
Full Text: DOI
[1] Shah, AV; Schade, H.; Vanecek, M.; Meier, J.; Vallat-Sauvain, E.; Wyrsch, N.; Kroll, U.; Droz, C.; Bailat, J., Thin-film silicon solar cell technology, Prog. Photovolt. Res. Appl., 12, 113-142, (2004)
[2] Geiszl, JF; Friedman, DJ; Ward, JS; Duda, A.; Olavarria, WJ; Moriarty, TE; Kiehl, JT; Romero, MJ; Norman, AG; Jone, KM, 40.8% efficient inverted triple-junction solar cell with two independently metamorphic junctions, Appl. Phys. Lett., 93, 123505, (2008)
[3] Atwater, HA; Polman, A., Plasmonics for improved photovoltaic devices, Nat. Mater., 9, 205213, (2010)
[4] Bermel, P.; Ghebrebrhan, M.; Chan, W.; Yeng, YX; Araghchini, M.; Hamam, R.; Marton, CH; Jensen, KF; Soljai, M.; Joannopoulos, JD; Johnson, SG; Celanovic, I., Design and global optimization of high-efficiency thermophotovoltaic systems, Opt. Express, 18, a314-a334, (2010)
[5] Lenert, A.; Bierman, DM; Nam, Y.; Chan, WR; Celanovic, I.; Soljacic, M.; Wang, EN, A nanophotonic solar thermophotovoltaic device, Nat. Nanotechnol., 9, 126-130, (2014)
[6] Sheng, X.; Johnson, SG; Michel, J.; Kimerling, LC, Optimization-based design of surface textures for thin-film Si solar cells, Opt. Express, 19, a841-a850, (2011)
[7] Dimitrova, DZ; Dua, CH, Crystalline silicon solar cells with micro/nano texture, Appl. Surf. Sci., 266, 1-4, (2013)
[8] Zaidi, S.H., Marquadt, R., Minhas, B., Tringe, J.W.: Deeply etched grating structures for enhanced absorption. In: 29th IEEE PVSC, pp. 1290-1293 (2002)
[9] Prentice, J., Coherent, partially coherent and incoherent light absorption in thin-film multilayer structures, J. Phys. D Appl. Phys., 33, 3139-3145, (2000)
[10] Springer, J.; Poruba, A.; Vanecek, M., Improved tree-dimensional optical model for thin-film silicon solar cells, J. Appl. Phys., 96, 5329-5337, (2004)
[11] Castrogiovanni, M., Nicosia, G., Rascuna’, R.: Experimental analysis of the aging operator for static and dynamic optimisation problems. In: 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems—KES 2007, 12-14 September 2007, Vietri sul Mare, Italy. Springer, LNCS, vol. 4694, pp. 804-811 (2007)
[12] Nicosia, G.: Immune Algorithms for Optimization and Protein Structure Prediction. Ph.D. Thesis (2005)
[13] Pardalos, P.M., Resende, M.G.C.: Handbook of Applied Optimization. Oxford University Press, Oxford (2002)
[14] Metropolis, N.; Rosenbluth, AW; Rosenbluth, MN; Teller, AH, Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087-1092, (1953)
[15] Powell, MJD, Developments of newuoa for minimization without derivatives, J. Numer. Anal., 28, 649-664, (2008) · Zbl 1154.65049
[16] Conca, P., Nicosia, G., Stracquadanio, G., Timmis, J.: Nominal-yield-area tradeoff in automatic synthesis of analog circuits: a genetic programming approach using immune-inspired operators. In: NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2009), July 29-August 1, San Francisco, CA, USA, IEEE Computer Society Press, pp. 399-406 (2009)
[17] Rios, LM; Sahinidis, NV, Derivative-free optimization: a review of algorithms and comparison of software implementations, J. Glob. Optim., 56, 1247-1293, (2013) · Zbl 1272.90116
[18] Wang, X.; Haynes, RD; Feng, Q., A multilevel coordinate search algorithm for well placement, control and joint optimization, Comput. Chem. Eng., 95, 75-96, (2016)
[19] Huyer, W.; Neumaier, A., Global optimization by multilevel coordinate search, J. Glob. Optim., 14, 331-355, (1999) · Zbl 0956.90045
[20] Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001) · Zbl 0970.90091
[21] Deb, K.; Gupta, H., Introducing robustness in multi-objective optimization, Evolut. Comput., 14, 463-494, (2006)
[22] Beyera, HG; Sendhoff, B., Robust optimization—a comprehensive survey, Comput. Methods Appl. Mech. Eng., 196, 3190-3218, (2007) · Zbl 1173.74376
[23] Iancu, DA; Trichakis, N., Pareto efficiency in robust optimization, Manag. Sci., 60, 130-147, (2014)
[24] Reyes-Sierra, M.; Coello Coello, CA, Multi-objective particle swarm optimizers: a survey of the state-of-the-art, Int. J. Comput. Intell. Res., 2, 287-308, (2006)
[25] Nebro, A.J., Durillo, J.J., García-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: Proceedings of IEEE Symposium Computational Intelligence MCDM, pp. 66-73 (2009)
[26] Deb, Kalyanmoy; Deb, Kalyanmoy, Multi-objective Optimization, 403-449, (2013), Boston, MA · Zbl 1165.90019
[27] Deb, K.; Jain, H., An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints, IEEE Trans. Evolut. Comput., 18, 577-601, (2014)
[28] Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolut. Comput., 6, 182-197, (2002)
[29] Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis: The Primer. Wiley, New York (2008) · Zbl 1161.00304
[30] Saltelli, A.; Tarantolaa, S.; Chana, KPS, A quantitative model-independent method for global sensitivity analysis of model output, Technometrics, 41, 39-56, (1999)
[31] Homma, T.; Saltelli, A., Importance measures in global sensitivity analysis of nonlinear models, Reliab. Eng. Syst. Saf., 52, 1-17, (1996)
[32] Campolongo, F.; Cariboni, J.; Saltelli, A., An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509-1518, (2007)
[33] Sobol, IM, Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates, Math. Comput. Simul., 55, 271-280, (2001) · Zbl 1005.65004
[34] Sobol, IM, On sensitivity estimation for nonlinear mathematical models, Mat. Mod., 2, 112-118, (1990) · Zbl 0974.00506
[35] Shinar, G.; Alon, U.; Feinberg, M., Sensitivity and robustness in chemical reaction networks, SIAM J. Appl. Math., 69, 977-998, (2009) · Zbl 1195.80023
[36] Patane’, A.; Santoro, A.; Costanza, J.; Nicosia, G., Pareto optimal design for synthetic biology, IEEE Trans. Biomed. Circ. Syst., 9, 555-571, (2015)
[37] Kitano, H., Towards a theory of biological robustness, Mol. Syst. Biol., 3, 1-7, (2007)
[38] Hafner, M.; Koeppl, H.; Hasler, M.; Wagner, A., Glocal robustness analysis and model discrimination for circadian oscillators, PLoS Comput. Biol., 5, 1-10, (2009)
[39] Jolliffe, I.T.: Principal Component Analysis. Springer, Berlin (1986)
[40] Hengl, S.; Kreutz, C.; Timmer, J.; Maiwald, T., Data-based identifiability analysis of non-linear dynamical models, Bioinformatics, 23, 2612-2618, (2007)
[41] Breiman, L.; Friedman, JH, Estimating optimal transformations for multiple regression and correlation, J. Am. Stat. Assoc., 80, 580-598, (1985) · Zbl 0594.62044
[42] Klein, S.; Repmann, T.; Brammer, T., Microcrystalline silicon films and solar cells deposited by PECVD and HWCVD, Solar Energy, 77, 893-908, (2004)
[43] Rech, B.; Kluth, O.; Repmann, T.; Roschek, T.; Springer, J.; Muller, J.; Finger, F.; Stiebig, H.; Wagner, H., New materials and deposition techniques for highly efficient silicon thin film solar cells, Solar Energy Mater. Solar Cells, 74, 439-447, (2002)
[44] Mailoa, JP; Lee, YS; Buonassisi, T.; Kozinsky, I., Textured conducting glass by nanosphere lithography for increased light absorption in thin-film solar cells, J. Phys. D Appl. Phys., 47, 85-105, (2014)
[45] Ward, L.: The Optical Constants of Bulk Materials and Films. IOP Publishing, Bristol (1994)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.