Bio-inspired metalens extracts depth from defocus
metalens. (Scale bar: 2µm) (C) Enlarged view of the
highlighted region in B, with nanopillars corresponding
to the 2 lens-phase profiles marked with red and blue.
(Scale bar: 500 nm.) (D) Side-view SEM image of the
edge of the metalens. (Scale bar: 200 nm.)
In each of their two principal eyes, jumping spiders sport a multi-tiered retina that simultaneously receives multiple images of a prey with different amounts of defocus, from which they can accurately decode the prey’s distance even with little brain power. Trying to emulate the multi-tiered retina optics, the researchers designed special metalens optics that split the light passing through an aperture, forming two differently defocused images at distinct regions of a single planar photosensor. Using fewer than 700 floating point operations per output pixel, they are then able to interpret the two images and build a depth map to represent object distance.
This depth extraction technique also known as “depth from defocus” is traditionally implemented with large cameras featuring motorized internal components that can capture differently focused images over time. But this cumbersome approach has speed limitations and is compute intensive.
In a paper titled “Compact single-shot metalens depth sensors inspired by eyes of jumping spiders” published in the Proceedings of the National Academy of Sciences, the researchers demonstrated a transparent metalens only 3mm in diameter assembled to off-the-shelf components to measure depth over a 10cm distance range, in a single acquisition shot. Although their proof-of-concept depth sensor prototype measured 4x4x10cm, in the future they could integrate the metalens with a purpose-built photosensor and housing for use on millimeter-scale, microwatts platforms such as microrobots, microsensor networks and small wearables.
subwavelength-spaced square nanopillars patterns
(visualized in red and blue) to form two images at
the same time. Courtesy of Qi Guo and Zhujun
Shi/Harvard University.
Lead author Qi Guo sees the close integration of metasurfaces with computational algorithms as a new way of creating computational sensors.
“Metalenses are a game-changing technology because of their ability to implement existing and new optical functions much more efficiently, faster, and with much less bulk and complexity than existing lenses,” professor Federico Capasso said.
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