Broadband Thermal Imaging using Meta-Optics

TL;DR

This paper presents a deep-learning aided inverse-design approach to create broadband meta-optics for LWIR imaging, significantly reducing chromatic aberrations and improving image quality.

Contribution

The work introduces a novel deep-learning framework for designing broadband meta-optics that combines local phase-engineering with global scatterer optimization.

Findings

Six-fold improvement in wavelength-averaged Strehl ratio

Successful fabrication and experimental validation of LWIR meta-optics

Enhanced image quality with reduced chromatic aberrations

Abstract

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 $\mu$m). 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) 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.