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      Compact single-shot metalens depth sensors inspired by eyes of jumping spiders

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          Significance

          Nature provides diverse solutions to passive visual depth sensing. Evolution has produced vision systems that are highly specialized and efficient, delivering depth-perception capabilities that often surpass those of existing artificial depth sensors. Here, we learn from the eyes of jumping spiders and demonstrate a metalens depth sensor that shares the compactness and high computational efficiency of its biological counterpart. Our device combines multifunctional metalenses, ultrathin nanophotonic components that control light at a subwavelength scale, and efficient computations to measure depth from image defocus. Compared with previous passive artificial depth sensors, our bioinspired design is lightweight, single-shot, and requires a small amount of computation. The integration of nanophotonics and efficient computation establishes a paradigm for design in computational sensing.

          Abstract

          Jumping spiders (Salticidae) rely on accurate depth perception for predation and navigation. They accomplish depth perception, despite their tiny brains, by using specialized optics. Each principal eye includes a multitiered retina that simultaneously receives multiple images with different amounts of defocus, and from these images, distance is decoded with relatively little computation. We introduce a compact depth sensor that is inspired by the jumping spider. It combines metalens optics, which modifies the phase of incident light at a subwavelength scale, with efficient computations to measure depth from image defocus. Instead of using a multitiered retina to transduce multiple simultaneous images, the sensor uses a metalens to split the light that passes through an aperture and concurrently form 2 differently defocused images at distinct regions of a single planar photosensor. We demonstrate a system that deploys a 3-mm-diameter metalens to measure depth over a 10-cm distance range, using fewer than 700 floating point operations per output pixel. Compared with previous passive depth sensors, our metalens depth sensor is compact, single-shot, and requires a small amount of computation. This integration of nanophotonics and efficient computation brings artificial depth sensing closer to being feasible on millimeter-scale, microwatts platforms such as microrobots and microsensor networks.

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          Most cited references40

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          A broadband achromatic metalens for focusing and imaging in the visible

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            Performing mathematical operations with metamaterials.

            We introduce the concept of metamaterial analog computing, based on suitably designed metamaterial blocks that can perform mathematical operations (such as spatial differentiation, integration, or convolution) on the profile of an impinging wave as it propagates through these blocks. Two approaches are presented to achieve such functionality: (i) subwavelength structured metascreens combined with graded-index waveguides and (ii) multilayered slabs designed to achieve a desired spatial Green's function. Both techniques offer the possibility of miniaturized, potentially integrable, wave-based computing systems that are thinner than conventional lens-based optical signal and data processors by several orders of magnitude.
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              Robotics. Programmable self-assembly in a thousand-robot swarm.

              Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                12 November 2019
                28 October 2019
                28 October 2019
                : 116
                : 46
                : 22959-22965
                Affiliations
                [1] aJohn A. Paulson School of Engineering and Applied Sciences, Harvard University , Cambridge, MA 02138;
                [2] bDepartment of Physics, Harvard University , Cambridge, MA 02138;
                [3] cDepartment of Electrical and Computer Engineering, National University of Singapore , 117580 Singapore;
                [4] dDepartment of Electrical Engineering and Computer Science, University of California, Berkeley , CA 94720
                Author notes
                2To whom correspondence may be addressed. Email: capasso@ 123456seas.harvard.edu or zhujunshi@ 123456g.harvard.edu .

                Contributed by Federico Capasso, September 24, 2019 (sent for review July 16, 2019; reviewed by David Stork and Jingyi Yu)

                Author contributions: Q.G., Z.S., F.C., and T.Z. designed research; Q.G., Z.S., and Y.-W.H. performed research; Q.G. and Z.S. analyzed data; and Q.G., Z.S., Y.-W.H., E.A., C.-W.Q., F.C., and T.Z. wrote the paper.

                Reviewers: D.S., Rambus Labs; and J.Y., University of Delaware.

                1Q.G. and Z.S. contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-4534-8249
                Article
                201912154
                10.1073/pnas.1912154116
                6859311
                31659026
                3b58dae9-727a-464b-a646-d5139d4eeb11
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                Page count
                Pages: 7
                Funding
                Funded by: DOD | USAF | AFMC | Air Force Office of Scientific Research (AFOSR) 100000181
                Award ID: FA9550-14- 396 1-0389
                Award Recipient : Zhujun Shi Award Recipient : Federico Capasso
                Funded by: DOD | USAF | AFMC | Air Force Office of Scientific Research (AFOSR) 100000181
                Award ID: FA9550-16-1-0156
                Award Recipient : Zhujun Shi Award Recipient : Federico Capasso
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IIS-1718012
                Award Recipient : Qi Guo Award Recipient : Emma Alexander Award Recipient : Todd Zickler
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: No. DGE1144152
                Award Recipient : Qi Guo Award Recipient : Emma Alexander Award Recipient : Todd Zickler
                Funded by: National Research Foundation Singapore (NRF) 501100001381
                Award ID: No. NRF- 400 CRP15-2015-03
                Award Recipient : Yaowei Huang Award Recipient : Cheng Wei Qiu
                Categories
                Physical Sciences
                Engineering

                depth sensor,metalens,jumping spider
                depth sensor, metalens, jumping spider

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