Neural network-based model for dual-junction solar cells
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...
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| Format: | Article |
| Language: | English |
| Published: |
2011
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| Online Access: | https://hdl.handle.net/10356/94184 http://hdl.handle.net/10220/7097 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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