The Inverse Design of Nanophotonic Structure Based on Neural Network

doi:10.13190/j.jbupt.2021-281

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (3): 112-116.doi: 10.13190/j.jbupt.2021-281

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The Inverse Design of Nanophotonic Structure Based on Neural Network

TIAN Liang, PAN Peng, LIU Yumin

School of Electronic Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2021-12-03 Online:2022-06-28 Published:2022-06-01

Abstract

Abstract: Compared with the method based on scanning parameters and optimization,neural network model can greatly improve the efficiency of structural inverse design with low design error. Two kinds of efficient neural network composite models are proposed: the composite model that introduces critic module based on generation adversarial idea and the composite model that adds simulator module based on multi-task learning. The two models to the inverse design problem of multilayer thin film absorption spectra after effective training on the data set simulated by the transfer matrix method, and the results of the inverse design of the two models are verified by the target spectra with special shapes. Finally, multi-task tandem network model is used to design a set of solar absorber structural parameters, which achieves an average absorption rate of 96% between 300nm and 1500nm.

Key words: nanophotonic structure, adversarial generation, multi-task learning, composite model

CLC Number:

TP183

TIAN Liang, PAN Peng, LIU Yumin. The Inverse Design of Nanophotonic Structure Based on Neural Network[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(3): 112-116.

References

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The Inverse Design of Nanophotonic Structure Based on Neural Network

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