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<p>Terahertz (THz) communication has emerged as one of the key technologies for sixth-generation (6G) wireless networks. Nevertheless, the transition to higher operational frequencies poses various challenges including high-speed digital-to-analog conversion (DACs) and analog-to-digital conversion (ADCs), heterogeneous integration of optoelectronic devices, resulting in an&#xa0;urgent need for solutions. In this paper, we demonstrate a groundbreaking THz analog differential operator driven by diffractive neural networks (DNN), implementing ultra-fast and high-throughput analog domain differential operations. The designed multilayer all-optical DNN composed of compact dielectric metasurfaces is trained with trigonometric functions to perform analog differential computing of complex input signals by approximating the differentiation of finite decompositions of time-domain function based on the Fourier transform theory, significantly improving integration, throughput, and processing speed. Our design has been experimentally validated to successfully implement single-direction differential operation on one-(1D) and two-dimensional&#xa0;(2D)&#xa0;signals with superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), providing a promising path for the development of integrated and ultrafast THz communication systems.</p>

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Terahertz all-optical analog differential operator based on diffractive neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月4日

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  • Dayu Cai,
  • Zihan Zhao,
  • Cong Wang,
  • Yang Li,
  • Qun Wu,
  • Guangwei Hu,
  • Xumin Ding

摘要

Terahertz (THz) communication has emerged as one of the key technologies for sixth-generation (6G) wireless networks. Nevertheless, the transition to higher operational frequencies poses various challenges including high-speed digital-to-analog conversion (DACs) and analog-to-digital conversion (ADCs), heterogeneous integration of optoelectronic devices, resulting in an urgent need for solutions. In this paper, we demonstrate a groundbreaking THz analog differential operator driven by diffractive neural networks (DNN), implementing ultra-fast and high-throughput analog domain differential operations. The designed multilayer all-optical DNN composed of compact dielectric metasurfaces is trained with trigonometric functions to perform analog differential computing of complex input signals by approximating the differentiation of finite decompositions of time-domain function based on the Fourier transform theory, significantly improving integration, throughput, and processing speed. Our design has been experimentally validated to successfully implement single-direction differential operation on one-(1D) and two-dimensional (2D) signals with superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), providing a promising path for the development of integrated and ultrafast THz communication systems.