Metasurface optics have demonstrated vast potential for implementing traditional optical components in an ultracompact and lightweight form factor. Metasurfaces, however, suffer from severe chromatic aberrations, posing serious limitations on their practical use. Existing approaches for circumventing this involving dispersion engineering are limited to small apertures and often entail multiple scatterers per unit cell with small feature sizes. Here, we present an alternative technique to mitigate chromatic aberration and demonstrate high-quality, full-color imaging using extended depth of focus (EDOF) metalenses and computational reconstruction. Previous EDOF metalenses have relied on cubic phase masks, where the image quality suffers from asymmetric artefacts. Here we demonstrate the use of rotationally symmetric masks, including logarithmic-aspherical, and shifted axicon masks, to mitigate this problem. Our work will inspire further development in achromatic metalenses beyond dispersion engineering and hybrid optical–digital metasurface systems.
Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2.
more » « less- NSF-PAR ID:
- 10360725
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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