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Neural network‐based model for dual‐junction solar cells
Author(s) -
Patra Jagdish C.
Publication year - 2011
Publication title -
progress in photovoltaics: research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.286
H-Index - 131
eISSN - 1099-159X
pISSN - 1062-7995
DOI - 10.1002/pip.985
Subject(s) - solar cell , atlas (anatomy) , artificial neural network , quantum tunnelling , computer science , dual (grammatical number) , biological system , electronic engineering , artificial intelligence , simulation , optoelectronics , physics , engineering , biology , art , paleontology , literature
Design and development of solar cells can be substantially improved by using models which can provide accurate estimation of complex device characteristics. The artificial neural network (NN)‐based models which learn from examples is an effective modeling technique that overcomes the deficiencies of conventional analytical techniques. In this paper, we propose NN‐based modeling techniques for estimation of behavior of dual‐junction (DJ) GaInP/GaAs solar cells involving complex phenomena, e.g., tunneling effect and complex interactions between the junctions. With extensive computer simulations we have compared performance of NN‐based models with that of a sophisticated device simulator, ATLAS form Silvaco. We have shown that the NN‐based models are able to estimate the solar cell characteristics close to that of the experimentally measured response. Compared with the response from ATLAS‐based models, the NN‐based models provide better results in estimation of tunneling phenomenon, determination of external quantum efficiency and I – V characteristics of DJ solar cells. Copyright © 2010 John Wiley & Sons, Ltd.