Inverse design of terahertz metagrating based on neural network

Jiajia Qian; Jun Zhou; Zheng Zhu; Zhenzhen Ge; Shuting Wu; Jinfeng Zhu

doi:10.1117/12.2605693

9 September 2021 Inverse design of terahertz metagrating based on neural network

Jiajia Qian, Jun Zhou, Zheng Zhu, Zhenzhen Ge, Shuting Wu, Jinfeng Zhu

Proceedings Volume 11909, Tenth International Symposium on Ultrafast Phenomena and Terahertz Waves (ISUPTW 2021); 119090X (2021) https://doi.org/10.1117/12.2605693
Event: Tenth International Symposium on Ultrafast Phenomena and Terahertz Waves (ISUPTW 2021), 2021, Chengdu, China

Abstract

In this paper, an inverse neural network based on deep learning is constructed to predict the metasurface structure of the designed terahertz metagrating. The transmittance spectra results from the numerical simulation of the metagrating were used as the input datasets for the inverse neural network, and the output is the corresponding metagrating structure parameters. After training, our inverse network can meet our expectations. The results show that some of the structural parameters predicted by the network are roughly consistent with the actual structural parameters, which indicates that the neural network can predict the corresponding structural parameters by given spectra. This has great application value, for example, it can be used to guide the design of metasurfaces for faster and more convenient purposes.

Citation Download Citation

Jiajia Qian, Jun Zhou, Zheng Zhu, Zhenzhen Ge, Shuting Wu, and Jinfeng Zhu "Inverse design of terahertz metagrating based on neural network", Proc. SPIE 11909, Tenth International Symposium on Ultrafast Phenomena and Terahertz Waves (ISUPTW 2021), 119090X (9 September 2021); https://doi.org/10.1117/12.2605693

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