Optimal design of thin-film plasmonic solar cells using differential evolution optimization algorithms

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Department of Electrical and Computer Engineering; Department of Materials Science and Engineering


An approach using a differential evolution (DE) optimization algorithm is proposed to optimize design parameters for improving the optical absorption efficiency of plasmonic solar cells (PSC). This approach is based on formulating the parameters extraction as a search and optimization process in order to maximize the optical absorption in the PSC. Determining the physical parameters of threedimensional (3-D) PSC is critical for designing and estimating their performance, however, due to the complex design of the PSC, parameters extraction is time and calculation intensive. In this paper, this technique is demonstrated for the case of commercial thin-film hydrogenated amorphous silicon (a-Si:H) solar photovoltaic cells enhanced through patterned silver nanodisk plasmonic structures. The DE optimization of PSC structures was performed to execute a real-time parameter search and optimization. The predicted optical enhancement (OE) in optical absorption in the active layer of the PSC for AM1.5 solar spectrum was found to be over 19.45% higher compared to the reference cells. The proposed technique offers higher accuracy and automates the tuning of control parameters of PSC in a time-efficient manner.

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© 2018. Publisher’s version of record: https://arxiv.org/abs/1807.00345

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