Spectral imaging paves the way for various fields, particularly in biomedical research. However, spectral imaging, mainly depending on spatial or temporal scanning, cannot achieve high temporal, spatial, and spectral resolution simultaneously. In this study, we demonstrated a silicon real-time ultraspectral imaging chip based on reconfigurable metasurfaces, comprising 155,216 ( 356 × 436 ) image-adaptive microspectrometers with ultra-high center-wavelength accuracy of 0.04 nm and spectral resolution of 0.8 nm. It is employed for imaging brain hemodynamics, and the dynamic spectral absorption properties of deoxyhemoglobin and oxyhemoglobin in a rat barrel cortex were obtained, which enlighten spectroscopy in vivo studies and other real-time applications.

Metasurfaces have an exceptional capacity to manipulate the phase, amplitude, polarization, or spectrum of light. However, unit cells, or meta‐atoms, of metasurfaces are conventionally designed using regular shapes, limiting performance improvement. The utilization of metasurfaces with freeform shaped meta‐atoms for on‐chip ultraspectral imaging is proposed, where the freeform shaped patterns are generated with controllable feature sizes and boundary curvatures for feasible fabrication. These patterns broaden design diversity and enrich metasurface‐unit spectral response with complicated Bloch modes, thus improving spectral imaging performance with enhanced spectrum recovery precision for broadband spectra and smaller center‐wavelength deviation for narrowband spectra. A snapshot on‐chip ultraspectral imaging, with 356 × 436 spectral pixels is experimentally demonstrated. Spectral resolution is state‐of‐the‐art, at 0.5 nm, and mean fidelity of spectral reconstruction for a standard color board reaches 98.78%. These results support future applications in the field of precise intelligent perception. Moreover, the generating method for freeform shaped patterns also benefits for the forward and inverse designs for high‐performance metasurfaces.

Modern face recognition systems usually combine RGB, depth, and infrared cameras to do face antispoofing, but they are still not robust enough to unknown 3D high-quality mask attack. In our work, we developed a snapshot hyperspectral image sensor based on metasurface nanostructures to obtain the high-precision hyperspectral information of the detected face, and we built a practical antispoofing face recognition system using our new sensor. Experiments show that our sensor can reconstruct the reflectance spectrum of human skin, and this spectral information captured by our sensor can be quite effective and robust to identify spoof faces. We attack our system with several types of spoof faces, and our system reaches 97.98% accuracy in real-world testing scenes.

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