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With the advancement of terahertz band applications, extensive research has been conducted on terahertz devices. Terahertz band biosensors find widespread use in biomedical microdetection; however, predicting terahertz spectrum and designing structures pose complex and time-consuming challenges. This article proposes an efficient deep learning method for optimizing the design of terahertz metasurface biosensors. The method employs three neural networks, utilizing spectral response as an intermediary to effectively map customized performance indicators onto geometric structural parameters. Test results demonstrate that the proposed design scheme can generate suitable structural parameters based on the required frequency and bandwidth of the analyte. These output parameters were subsequently simulated and validated using electromagnetic simulation software, yielding results consistent with predictions. This method of using neural networks instead of electromagnetic simulation can be applied to the study of spectrum prediction and inverse design of terahertz devices, providing more possibilities for the future application of terahertz devices.

The gap plasmon resonance can generate a local enhanced electric field and make the electromagnetic field energy mostly confined in the microfluidic, thereby increasing the interaction between the detection sample and terahertz wave. In this paper, a five-layer microfluidic terahertz biosensor based on gap plasmon resonance is presented. The simulation results show that the designed biosensor can realize a resonant absorption peak with near-perfect absorption at the frequency of 0.655 THz and high refractive index sensitivity of 191.3 GHz/RIU. In addition, the biosensor possesses the characteristics of polarization insensitivity and 50° incidence angle insensitivity due to the symmetry of the metal metasurface, which make the biosensor can be widely used in the biological label-free detection, medical trace element detection.

A dynamically tunable plasmon induced transparency planar structure with double layers hollow-cross graphene array is proposed in this paper. Due to energy oscillating back and forth between two weak hybridized states made up of upper and lower graphene, a transparent window during two resonance points is generated, and the PIT curve fits well with the Lorentz model. Via tuning the Fermi energy of graphene, a transparent window with the maximum transmissivity of 98.3% and modulation depth of 17.3% can be dynamically manipulated. In addition, the amplitude and frequency of the transmittance can be controlled by electrical doping without changing the structure. The structure provides a new scenario for innovative designs of plasmon-induced transparent structures. The proposed graphene-based PIT metamaterial shows promising applications of slow light technology, switchers, and filters in THz frequencies.