Neural nano-optics for high-quality thin lens imaging

Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung Hwan Baek, Arka Majumdar, Felix Heide

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6493
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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