Momentum‐Space Tunable Metasurfaces for Switchable Image Processing
Kai Zhang, Shuo Wang, Jumin Qiu, Muyi Yang, Tingting Liu, Shuyuan Xiao, Isabelle Staude, Thomas Pertsch, Yu Wang, Chengjun Zou
Abstract
Abstract The exceptional ability of optical metasurfaces to manipulate light has enabled integrated analog computing and image processing in ultracompact, energy‐efficient platforms that support high speeds. To date, metasurfaces have demonstrated various analog processing functions, including differentiation, convolution, and classification. However, a fundamental limitation of existing designs is their static functionality, which restricts adaptability to diverse application scenarios. To address this challenge, momentum‐space reconfigurable metasurfaces operating in the near‐infrared range are experimentally demonstrated, capable of switchable image processing functions including image differentiation and bright‐field imaging. These meta‐devices are achieved by integrating nematic liquid crystals with silicon metasurfaces that support resonances of quasi‐bound states in the continuum (quasi‐BICs). The quasi‐BIC modes enable further design freedom over the angular dispersion of metasurfaces. The results showcase an electrically tunable, CMOS‐compatible approach to reconfigurable optical computing, offering significant potential for applications such as online training of diffractive neural networks, machine vision, and augmented reality.