Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality

• Published in: Journal of global optimization 2018-11, Vol.72 (3), p.491-515
• Main Authors: Patanè, Andrea, Santoro, Andrea, Romano, Vittorio, Magna, Antonino La, Nicosia, Giuseppe
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; CAD; Coherent scattering; Computer aided design; Computer Science; Computer simulation; Design optimization; Design parameters; Dielectric films; Efficiency; Light; Mathematical models; Mathematical optimization; Mathematics; Mathematics and Statistics; Operations Research/Decision Theory; Optimization; Parameter identification; Parameter sensitivity; Pareto efficiency; Pareto optimum; Photon absorption; Photonics; Photovoltaic cells; Qualitative analysis; Quantum efficiency; Real Functions; Sensitivity analysis; Silicon; Silicon wafers; Solar cells; Thin films
• ISSN: 0925-5001; 1573-2916
• DOI: 10.1007/s10898-018-0639-9

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.