A comparison of genetic and particle swarm optimization for contact formation in high-performance silicon solar cells
In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several...
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| creator | Hyun-Soo Kim Morris, B.G. Seung-Soo Han May, G.S. |
| description | In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several input factors and solar cell efficiency. Subsequently, both genetic algorithms and particle swarm optimization are used to identify the optimal process conditions to maximize cell efficiency. The results of the two approaches are compared, and the optimized efficiency found via the particle swarm method was slightly larger than the value determined via genetic algorithms. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller standard deviations, implying greater consistency in recipe generation. |
| doi_str_mv | 10.1109/IJCNN.2008.4633999 |
| format | Conference Proceeding |
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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2161-4393 |
| ispartof | 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008, Vol.10, p.1531-1535 |
| issn | 2161-4393 1522-4899 2161-4407 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Gallium Genetic algorithms Neurons Object oriented modeling Optimization Photovoltaic cells |
| title | A comparison of genetic and particle swarm optimization for contact formation in high-performance silicon solar cells |
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