Neural Network‐Assisted End‐to‐End Inverse Design for Polarimetric Microspectrometer

Abstract Metasurfaces represent a cutting‐edge platform for the miniaturization of spectral and polarimetric sensing applications. However, achieving multi‐channel control has been a significant challenge for conventional design methodologies. This work introduces an end‐to‐end multi‐channel inverse design framework capable of directly optimizing metasurface geometry for precise optical field manipulation. A cascaded metasurface system can be conceptualized as an optical diffractive neural network. By developing a deep neural network that correlates the optical response of meta‐atoms with their structural properties, a differentiable pipeline is established to drive metasurface designs toward multi‐functional requirements. Leveraging this approach, a polarization‐independent microspectrometer is designed, which is unaffected by the polarization state of incident light and theoretically has a spectral resolution down to 2 nanometers across the visible spectrum. Furthermore, a polarization‐dependent microspectrometer is demonstrated to simultaneously capture both spectral and polarization information from a single exposure. This device maintains the same high spectral resolution and also provides precise determination of the polarization angle. Such polarization‐sensitive microspectrometer has potential applications in the detection of chiral substances. The results will accelerate the progress of meta‐optics, with implications for spectro‐polarimetric detection, multi‐target holographic displays, parallel optical information processing, and optical computing.