Machine learning in design of broadband terahertz absorbers based on composite structures

Ding, Zhipeng; Su, Wei; Luo, Yinlong; Ye, Lipengan; Wu, Hong; Yao, Hongbing

doi:10.1016/j.matdes.2023.112215

Cited by 16 publications

(4 citation statements)

References 58 publications

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“…Currently, various deep learning technologies are blooming for scientific discovery and intelligent design, such as artificial neural networks and chat generative pre-trained transformer (ChatGPT), [40][41][42] which should have the potential to be used as state-of-the-art tools of optical design to investigate such kind of metasurfaces. [43][44][45][46][47][48][49][50][51][52][53][54][55][56] In view of these two aspects, we propose a deep learning scheme based on the transformer model to boost the design of the all-dielectric SERS metasurface, which consists of the forward neural network (FNN) and inverse neural network (INN), as shown in Figure 4. The FNN is employed to replace the time-consuming optical simulation.…”

Section: Advanced Design Of Sers Metasurfaces By Transformer-based De...mentioning

confidence: 99%

All‐Dielectric SERS Metasurface with Strong Coupling Quasi‐BIC Energized by Transformer‐Based Deep Learning

Chen,

Li,

Liu

et al. 2023

Advanced Optical Materials

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Researchers have paid great attention to pursuing metal‐free nanostructures as alternatives to metallic counterparts in the field of surface‐enhanced Raman scattering (SERS) for label‐free sensing. However, these metal‐free investigations are hindered by the tiny enhancement factor of the local near field. Here, the design of all‐dielectric SERS metasurfaces is proposed with quasi‐bound states in the continuum (Q‐BIC), inspired by transformer‐based deep learning. By manipulating the incident angle, the mechanism of strong coupling Q‐BIC is introduced with a large Rabi splitting of ≈105 meV, which opens a bandgap and forms an anti‐crossing behavior. Compared to conventional approaches, the strong coupling Q‐BIC scheme not only boosts an extraordinary SERS enhancement factor of ≈107 but also extends the field‐enhancing scale up to ten‐fold. The theoretical optimization implies overwhelming dominance versus the conventional metallic nanostructure design for SERS. The study denotes an approach to utilize the strong coupling effects of Q‐BIC in all‐dielectric SERS metasurfaces and will provide essential design guides for more powerful sensing applications based on SERS.

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Section: Advanced Design Of Sers Metasurfaces By Transformer-based De...mentioning

confidence: 99%

All‐Dielectric SERS Metasurface with Strong Coupling Quasi‐BIC Energized by Transformer‐Based Deep Learning

Chen,

Li,

Liu

et al. 2023

Advanced Optical Materials

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“…Since Landy et al 23 first introduced metasurface absorbers, a growing number of high-performance absorbers based on metasurfaces have been proposed and demonstrated. 24–28…”

Section: Introductionmentioning

confidence: 99%

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption

Ding,

Su,

Luo

et al. 2024

Nanoscale

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“…13,14 Consequently, metasurfaces have found successful applications across diverse domains such as planar lenses, holography, optical cloaking, and beam shaping. 15–20…”

Section: Introductionmentioning

confidence: 99%

Thermal controlled multi-functional metasurface for freely switching of absorption, reflection, and transmission

Ding,

Su,

et al. 2024

Phys. Chem. Chem. Phys.

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Machine learning in design of broadband terahertz absorbers based on composite structures

Cited by 16 publications

References 58 publications

All‐Dielectric SERS Metasurface with Strong Coupling Quasi‐BIC Energized by Transformer‐Based Deep Learning

All‐Dielectric SERS Metasurface with Strong Coupling Quasi‐BIC Energized by Transformer‐Based Deep Learning

Metasurface inverse designed by deep learning for quasi-entire terahertz wave absorption

Thermal controlled multi-functional metasurface for freely switching of absorption, reflection, and transmission

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