D-Flat: A Differentiable Flat-Optics Framework for End-to-End Metasurface Visual Sensor Design
Abstract
Optical metasurfaces are planar substrates with custom-designed, nanoscale features that selectively modulate incident light with respect to direction, wavelength, and polarization. When coupled with photodetectors and appropriate post-capture processing, they provide a means to create computational imagers and sensors that are exceptionally small and have distinctive capabilities. We introduce D-Flat, a framework in TensorFlow that renders physically-accurate images induced by metasurface optical systems. This framework is fully differentiable with respect to metasurface shape and post-capture computational parameters and allows simultaneous optimization with respect to almost any measure of sensor performance. D-Flat enables simulation of millimeter to centimeter diameter metasurfaces on commodity computers, and it is modular in the sense of accommodating a variety of wave optics models for scattering at the metasurface and for propagation to photosensors. We validate D-Flat against symbolic calculations and previous experimental measurements, and we provide simulations that demonstrate its ability to discover novel computational sensor designs for two applications: single-shot depth sensing and single-shot spatial frequency filtering.
Cite
@article{arxiv.2207.14780,
title = {D-Flat: A Differentiable Flat-Optics Framework for End-to-End Metasurface Visual Sensor Design},
author = {Dean S. Hazineh and Soon Wei Daniel Lim and Zhujun Shi and Federico Capasso and Todd Zickler and Qi Guo},
journal= {arXiv preprint arXiv:2207.14780},
year = {2022}
}