In recent years, intelligent design methods for metamaterial devices based on neural network technology have become a prominent research focus. The conventional design process of metamaterial polarization converters primarily depends on the theoretical knowledge and simulation expertise of designers, requiring substantial time and computational resources during parameter optimization. Neural networks enable the learning of potential mapping rules from extensive data, facilitating intelligent optimization of structural parameters. This design focuses on developing a neural network-based polarization converter in the terahertz band, which enhances design efficiency and addresses the research gap in neural network applications for terahertz band metamaterial design. This research establishes a novel technical approach for the intelligent design of terahertz functional devices. The designed metasurface polarization converter demonstrates significant potential in communication, imaging, remote sensing, and electronic countermeasure applications.

This investigation presents a reflective terahertz polarization converter utilizing a three-layer structure consisting of a metal aluminum pattern, a polyimide dielectric substrate, and a metal aluminum plate. The converter underwent modeling and simulation using CST Studio Suite 2020 electromagnetic simulation software, generating a dataset of structural parameters and corresponding performance metrics. The optimization process employed an innovative tandem neural network architecture. The implementation began with constructing a forward prediction network, followed by fixing its weights and cascading it with an inverse design network to create a complete tandem network. The trained tandem neural network enables the extraction of polarization converter geometric parameters from intermediate network layers by inputting the target polarization conversion rate (PCR). This approach substantially enhanced design efficiency while maintaining high prediction accuracy.

The neural network training outcomes demonstrate favorable convergence characteristics for both the feedforward prediction network and the tandem network. The feedforward network exhibited a stable learning curve during training, with the mean squared error (MSE) converging to 0.0015 and stabilizing after 200 epochs. The tandem network achieved faster convergence, with MSE stabilizing at 0.0016 within 50 epochs. Performance evaluation on the test set revealed final MSE values of 0.00147 and 0.00286 for the feedforward prediction network and tandem network, respectively, validating the network architecture’s effectiveness. Numerical simulation results demonstrate that with optimized structural parameters (p=90 μm, L=30 μm, W=7.3 μm, d=12.7 μm, α=59.2°, t₁=31.3 μm, t₂=0.5 μm) obtained through a super-Gaussian objective function, the metasurface polarization converter exhibits exceptional performance characteristics (Fig. 7). The network prediction curve closely aligns with the software simulation curve, confirming the network’s capability for on-demand design (Fig. 8). Under optimized structural parameters, the converter demonstrates broadband characteristics at 0° incidence within 0.8?1.8 THz, maintaining PCR above 0.8. The device maintains efficiency above 0.8 within 0.8?1.5 THz even at large-angle incidence (50°) (Fig. 9). The polarization converter shows excellent angle stability and polarization-insensitive characteristics. The current distribution diagram provides enhanced understanding of the polarization conversion mechanism (Fig. 10).

A reflective metasurface polarization converter comprising a bottom aluminum plate, a dielectric substrate interlayer, and a top M-shaped metallic aluminum plate is proposed. The structural parameters underwent optimization using a tandem neural network to achieve optimal polarization conversion efficiency. The trained network enables rapid prediction from target conversion efficiency spectra to structural parameters, significantly reducing design time. For a target conversion efficiency spectrum based on a super-Gaussian function (maximum value of 0.85 and frequency range of 0.8?1.40 THz), the network predicted the following structural parameters at 50° incidence: p=90 μm, L=30 μm, W=7.3 μm, d=12.7 μm, α=59.2°, t₁=31.3 μm, and t₂=0.5 μm. CST Studio Suite 2020 simulations confirm the excellent performance of the resulting metasurface polarization converter. Under normal incidence, it achieves efficient polarization conversion within 0.8?1.8 THz, maintaining efficiency above 0.8. At 50° incidence, the high conversion efficiency bandwidth ranges from 0.8 to 1.5 THz, maintaining conversion efficiency above 0.8. The conversion mechanism relies on magnetic dipole resonance effects. This optimization method combines high accuracy with reduced design cycles. The designed converter shows promising applications in communication, stealth technology, imaging, remote sensing, sensing, display, and electronic countermeasures.