Inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Hu, Yanwen; Ma, Yaodong; Zhang, Tingrong; Li, Shoudong; Chen, Xiaoqiang

doi:10.1088/1361-6463/acefdf

Cited by 2 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FEIDM based on deep learning is introduced to predict the structural parameters of the metasurface in the section. The study is a continuation of our previous work [29].…”

Section: Design Methodsmentioning

confidence: 66%

“…The unit cell of the transmission-type linear-to-circular polarization control metasurface is shown in figure 3 [29]. The metasurface comprises two layers.…”

Section: Example Application 31 Unit Cell Of the Metasurfacementioning

confidence: 99%

“…In [29], P L was set to 30 mm. Therefore, the frequency range that the network model can predict is limited.…”

Section: Example Application 31 Unit Cell Of the Metasurfacementioning

confidence: 99%

See 2 more Smart Citations

Frequency-extended inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Hu,

Ma,

Zhang

et al. 2024

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

Due to the parameter range limitations of the training dataset, traditional inverse prediction network models can only predict structure parameters of the metasurface within a limited frequency range. When the given design targets exceed the prediction range of network models, the predicted results will not match the actual results. This paper proposes a frequency-extended inverse design method (FEIDM) based on deep learning to address the problem. The method can automatically collect the required data and train the network model based on the center working frequency of the design targets, thereby achieving accurate prediction of metasurface structural parameters and effectively reducing labor and computational costs. Taking the transmission-type linear-to-circular polarization control metasurface as an example, the unit cell of the metasurface is first established in the paper. The structural parameters and corresponding electromagnetic parameters were collected without changing the unit size of the metasurface, and an initial inverse prediction network model (IIPNM) was constructed. The research results indicate that its predictable center working frequency range is 3-5.5 GHz. Using the design concept proposed in this paper, a program was constructed, it can automatically achieve data collection, target extraction, network model training, and prediction. Four given design targets were predicted. Among them, the center working frequencies of the three design targets are outside the initial predictable range. The predicted results meet the requirements of the given target, verifying the effectiveness of the proposed scheme. Finally, a set of parameters was selected to fabricate, and the experimental results are consistent with the simulation results. The research results can provide a reference for the efficient prediction of metasurface structural parameters over a wide frequency band.

show abstract

“…FEIDM based on deep learning is introduced to predict the structural parameters of the metasurface in the section. The study is a continuation of our previous work [29].…”

Section: Design Methodsmentioning

confidence: 66%

“…The unit cell of the transmission-type linear-to-circular polarization control metasurface is shown in figure 3 [29]. The metasurface comprises two layers.…”

Section: Example Application 31 Unit Cell Of the Metasurfacementioning

confidence: 99%

See 1 more Smart Citation

Frequency-extended inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Hu,

Ma,

Zhang

et al. 2024

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

show abstract

Neural-network enhanced adaptive design for ultrathin single-substrate transmissive wide-band optimized polarization converters

Ding,

Chen,

Luo

et al. 2024

Opt. Express

View full text Add to dashboard Cite

A neural-network enhanced adaptive design for ultrathin, single-substrate polarization converters optimized for wideband transmission is proposed. This research utilizes machine learning to tackle the inverse design challenge, aiming for customizable relative bandwidths of polarization conversion up to 20%. The design incorporates only a dielectric layer surrounded by two metallic layers. A sophisticated concatenated network architecture is central to this work, inversely designing converters for the 10-16 GHz band and achieving targeted bandwidths of 10%-20% at various frequencies with transmission amplitudes exceeding 0.9. One sample has been constructed and measured. This structure enables 90° cross-polarization conversion with a transmission bandwidth of 20%, with an optimized thickness of just 0.09λ0. Validation tests on the prototype demonstrate less than 2% error, confirming the method's precision and its potential for broader applications in metamaterial and metasurface design.

show abstract

Inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Cited by 2 publications

References 34 publications

Frequency-extended inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Frequency-extended inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Neural-network enhanced adaptive design for ultrathin single-substrate transmissive wide-band optimized polarization converters

Contact Info

Product

Resources

About