Abstract
Polarization, a fundamental property of terahertz (THz) waves, can be controlled using metasurfaces for signal transmission and sensitive measurements. However, conventional methods for designing polarization-conversion metasurfaces require numerous numerical simulation iterations and trial and error attempts that are computationally intensive and time-consuming, necessitating a more rapid and efficient approach. This study proposes a transformer-based method for the inverse design of terahertz polarization converters; the approach can accurately design the corresponding metasurface based on the target spectrum. The model also comprises a forward network that can precisely and intuitively predict the meta-atoms spectra with high simulation speed and reduced computational cost. Using this approach, two ultra-broadband polarization conversion devices were designed in approximately 0.006 s within a target frequency range of 0.5–4 THz, with polarization conversion ratios greater than 90% within 1.23–3.19 THz and 1.18–3.24 THz. Additionally, the average cross-polarization ratio was greater than 50%. This study provides insights into designing terahertz polarization converters, which are applicable in terahertz communications, polarization imaging, and biomedical procedures.
| Original language | English |
|---|---|
| Article number | 130434 |
| Journal | Optics Communications |
| Volume | 559 |
| DOIs | |
| State | Published - 15 May 2024 |
Keywords
- Deep learning
- Metasurface
- Polarization converter
- Ultra-broadband
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