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Residual Parallel Neural Networks Aided Inverse Design for Multifunctional Reconfigurable Metamaterial Perfect Absorbers

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Abstract

In recent years, significant strides have been made in the inverse design of metamaterial perfect absorbers (MPAs) using deep learning techniques. However, this progress has been hindered by the functional homogeneity arising from the structural uniformity of the inverse-designed MPAs. In this paper, we address this limitation by designing reconfigurable MPAs (RMPAs) with three distinct structures and propose a residual parallel neural network (RPNN) that incorporates the optimized residual fully connected neural network (RFC-NN) for the inverse design of multifunctional MPAs. The trained RPNN accurately predicts the structural parameters and their corresponding absorption spectra with remarkable precision, yielding R2 values of 0.9981 and 0.9928, respectively. With this model, we successfully inverted the design of MPAs with three functions: broadband absorption, dual-band absorption, and triple-band absorption properties. A particularly noteworthy achievement was the realization of absorption bandwidth shifts using liquid crystal (LC) materials. Our RPNN showcases its proficiency in designing RMPAs with multifunctionality, all within a single network model. This marks a significant advancement over previous research methodologies. The proposed methodology holds great promise in diverse applications such as solar energy harvesting, detection, and filtration.

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Funding

This work has been supported by National Natural Science Foundation of China (Grant Nos. 62175070 and 61875057); GuangDong Basic and Applied Basic Research Foundation (Grant No. 2021A1515010352, No. 2021A1515012652, No. 2023A1515012966); and The Science and Technology Program of Guangzhou (Grant No. 202201010340).

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510006, China

    Shuqin Wang, Zhongchao Wei, Ruihuan Wu, Qiongxiong Ma & Jianping Guo

  2. Guangdong Provincial Key Laboratory of Antenna and Radio Frequency Technology, Guangdong Shenglu Telecommunication Tech. Co., Ltd., Foshan, Guangdong, 430072, China

    Wen Ding

  3. Guangdong Education Center of Optoelectronic Information Technology, South China Normal University, Guangzhou, 510006, China

    Jianping Guo

Authors
  1. Shuqin Wang
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  2. Zhongchao Wei
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  3. Ruihuan Wu
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  4. Qiongxiong Ma
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  5. Wen Ding
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  6. Jianping Guo
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Contributions

Shuqin Wang, Zhongchao Wei, Ruihuan Wu, Qiongxiong Ma, Jianping Guo, and Wen Ding contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Shuqin Wang. Shuqin Wang wrote the main manuscript text, while Shuqin Wang, Zhongchao Wei, Ruihuan Wu, Qiongxiong Ma, and Jianping Guo prepared Figs. 1–8, 10, and 11. Shuqin Wang and Wen Ding prepared Fig. 9. Data acquisition was performed by Shuqin Wang, Zhongchao Wei, Jianping Guo, and Wen Ding. Jianping Guo supervised the project. The investigation and software were performed by Shuqin Wang, Zhongchao Wei, Wen Ding, and Jianping Guo. Funding acquisition was provided by Zhongchao Wei, Ruihuan Wu, and Qiongxiong Ma. All authors reviewed the manuscript.

Corresponding author

Correspondence to Jianping Guo.

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This is an observational study. The XYZ Research Ethics Committee has confirmed that no ethical approval is required.

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Wang, S., Wei, Z., Wu, R. et al. Residual Parallel Neural Networks Aided Inverse Design for Multifunctional Reconfigurable Metamaterial Perfect Absorbers. Plasmonics (2023). https://doi.org/10.1007/s11468-023-02133-z

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