Design and optimization of terahertz metamaterial sensor with high sensing performance

Sun, Ran; Li, Wenyu; Meng, Tao; Zhao, Guozhong

doi:10.1016/j.optcom.2021.127051

Cited by 30 publications

(4 citation statements)

References 33 publications

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“…This result showed that the designed structure can be used as a refractive index sensor [24,25]. The sensitivity (S) is an important index of a refractive index sensor, and it can be expressed by ∆f /∆n [26]. As shown in Figure 11b, there is a larger S on the low frequency (f 1 ) and a smaller S on the high frequencies (f 2 and f 3 ).…”

Section: Resultsmentioning

confidence: 82%

Wideband Reflector and Analogue Electromagnetically Induced Reflection in Metamaterials

Huang

Ning

et al. 2021

Crystals

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Metamaterials are highly demanded for advanced applications in absorbers, sensors, and filters. However, metamaterial reflectors, especially broadband reflectors, remain challenging. In this paper, we theoretically investigate a wideband metamaterial reflector which consists of cross shaped graphene strips and a silica (SiO2) substrate. The cross shaped graphene strips are coated on the top of the structure, and the cross shape rotated 45° graphene strips are spun on the bottom of it. The calculated reflection can be tuned through length and width of the graphene strips. By comparison, not only broadband reflection but also analogue electromagnetically induced reflection (EIR) can be realized. Moreover, the structure can generate a bi-directional broadband reflection of insensitive polarization. This kind of bi-directional reflector at microwave frequencies is obtained because the top and bottom graphene strip structures are similar. We employ the electric field distribution of the designed structure to elucidate the mechanism of the analogue EIR effect. We further discuss the influence of incident angle on the analogue EIR effect. Such a bi-directional reflector can be potentially applied to a wideband reflector, antenna, and sensor.

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Section: Resultsmentioning

confidence: 82%

Wideband Reflector and Analogue Electromagnetically Induced Reflection in Metamaterials

Huang

Ning

et al. 2021

Crystals

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show abstract

“…In addition, the optimized design has reached the application level. It can be seen from figure 8 that the EIT window has higher transmittance, lower loss, stronger out-of-band inhibition ability, and wider operating bandwidth, compared with the EIT spectrum in the literature [38]. Worth noting that, if the traditional simulation software is used to optimize the design of EIT metamaterials, the dimensionality of the structural parameter change is relatively single.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for electromagnetically induced transparency (EIT) metasurface optimization design

Zhu¹,

Zhang²,

Guo³

et al. 2022

J. Phys. D: Appl. Phys.

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In order to accelerate the design process of electromagnetically induced transparency (EIT) metasurface, a deep learning-based EIT metasurface design method is proposed, where the spectral profile of EIT metasurface can be predicted by the forward prediction process, and the EIT metasurface geometry parameters based on the target spectral profile can be obtained by the inverse design process. In the inverse design process, a cascaded convolutional neural network (CNN) consisting of one-dimensional convolutional layer, a batch normalization layer, a pooling layer and an exponential linear unit (ELU) activation function is employed. Each convolutional neural network is designed to achieve feature extraction for spectra. The inverse network achieves low mean square errors (MSE), with MSE of 0.011 on the validation sets. After training, the model can more accurately predict the parameters with error of 0.3μm. Such method is more efficient and saves computing resources, allowing designers to focus on the target spectra. More importantly, it can be extended to the design of arbitrary metasurface.

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“…The model is applied to EIT metasurface structures to confirm the feasibility of the proposed neural network. Compared with conventional design approaches [44,45], the proposed deep learning scheme requires relatively low expertise of metasurface. The designer only needs to focus on the target EIT spectra to achieve the inverse design.…”

Section: Discussionmentioning

confidence: 99%

Inverse design of electromagnetically induced transparency(EIT) metasurface based on deep convolutional generative adversarial network

Zhu,

Zhang,

Dong

et al. 2023

Phys. Scr.

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With the increasing complexity of electromagnetically induced transparency (EIT) metasurface structure and the limitations of traditional optimization methods, there is an urgent need for an advanced design approach to accelerate the design of complex EIT metasurface. In this study, we propose an improved deep learning model based on deep convolutional generative adversarial network (DCGAN) to simplify the design process of EIT metasurface. The proposed model enables the optimization of metasurface with eight structural parameters, obtaining single-band or dual-band EIT effects for y polarization incidence. The training of the network model achieves convergence with a mean square error (MSE) of 0.2 for the generator and 0.41 for the discriminator. The average errors between the predicted results and the target parameters are within 0.6 μm. The relative spectral error (RSE) is utilized to quantify the deviation between the simulated spectra obtained from the predicted structures and the target spectra, exhibiting a minimum RSE of 6.26%. Furthermore, comparisons between the proposed model and two other convolutional neural networks validate the superior prediction capability and higher accuracy of our proposed model.

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Design and optimization of terahertz metamaterial sensor with high sensing performance

Cited by 30 publications

References 33 publications

Wideband Reflector and Analogue Electromagnetically Induced Reflection in Metamaterials

Wideband Reflector and Analogue Electromagnetically Induced Reflection in Metamaterials

Deep learning for electromagnetically induced transparency (EIT) metasurface optimization design

Inverse design of electromagnetically induced transparency(EIT) metasurface based on deep convolutional generative adversarial network

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