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Broadband thermal imaging using meta-optics.

Luocheng HuangZheyi HanAnna Wirth-SinghVishwanath SaragadamSaswata MukherjeeJohannes E FröchQuentin A A TanguyJoshua RollagRicky GibsonJoshua R HendricksonPhilip W C HonOrrin KignerZachary CoppensKarl F BöhringerAshok VeeraraghavanArka Majumdar
Published in: Nature communications (2024)
Subwavelength diffractive optics known as meta-optics have demonstrated the potential to significantly miniaturize imaging systems. However, despite impressive demonstrations, most meta-optical imaging systems suffer from strong chromatic aberrations, limiting their utilities. Here, we employ inverse-design to create broadband meta-optics operating in the long-wave infrared (LWIR) regime (8-12 μm). Via a deep-learning assisted multi-scale differentiable framework that links meta-atoms to the phase, we maximize the wavelength-averaged volume under the modulation transfer function (MTF) surface of the meta-optics. Our design framework merges local phase-engineering via meta-atoms and global engineering of the scatterer within a single pipeline. We corroborate our design by fabricating and experimentally characterizing all-silicon LWIR meta-optics. Our engineered meta-optic is complemented by a simple computational backend that dramatically improves the quality of the captured image. We experimentally demonstrate a six-fold improvement of the wavelength-averaged Strehl ratio over the traditional hyperboloid metalens for broadband imaging.
Keyphrases
  • high resolution
  • deep learning
  • machine learning
  • artificial intelligence
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  • photodynamic therapy
  • optical coherence tomography
  • climate change
  • convolutional neural network