Skip to main navigation Skip to search Skip to main content

Designing ultra-broadband terahertz polarization converters based on the transformer model

  • Bingxuan Mao
  • , Honghao Chang
  • , Xiaohua Xing
  • , Qiankun Zhang
  • , Die Zou
  • , Yin Liu
  • , Jianquan Yao
  • , Haixia Bi
  • , Liang Wu
  • Tianjin University
  • Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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 languageEnglish
Article number130434
JournalOptics Communications
Volume559
DOIs
StatePublished - 15 May 2024

Keywords

  • Deep learning
  • Metasurface
  • Polarization converter
  • Ultra-broadband

Fingerprint

Dive into the research topics of 'Designing ultra-broadband terahertz polarization converters based on the transformer model'. Together they form a unique fingerprint.

Cite this