Momentum-Space Tunable Metasurfaces for Switchable Image Processing

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.