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Designing a multi-quantum-dot-doped wavelength down-shifting model of c-Si solar cells by using machine learning

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Abstract

The efficiency of solar cell energy conversion may be significantly improved by better exploitation of the original solar spectrum. In this paper, we propose a multi-quantum-dot-doped wavelength down-shifting (WDS) model that can reduce the energy loss resulting from the mismatch between the solar spectrum and the solar cell material’s responsivity. The WDS layer can nearly perfectly absorb the photons in the range of 300 to 700 nm and then re-emit them at a longer wavelength. The distribution of quantum dots in the WDS layer is provided based on analysis of its spectral properties with a machine learning regression algorithm. Finally, the relationship between the optimizing ability of the model and the Stokes shift of the quantum dots is also discussed. Ideally, our model can provide up to 29% improvement to the external quantum efficiency of c-Si solar cells, which fully demonstrates the potential of our model.

© 2020 Optical Society of America

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