Evidence map›Paper›PMID 39633861›Full record

ArticleNanophotonics (Berlin, Germany)2023

Dual-band optical collimator based on deep-learning designed, fabrication-friendly metasurfaces.

Akira Ueno, Hung-I Lin, Fan Yang, Sensong An, Louis Martin-Monier, Mikhail Y Shalaginov, Tian Gu, Juejun Hu

Abstract read
In one paragraph

Article in Nanophotonics (Berlin, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Akira UenoDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID https://orcid.org/0000-0003-0171-5836
Hung-I LinDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Fan YangDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID https://orcid.org/0000-0002-5185-5629
Sensong AnDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID https://orcid.org/0000-0003-4098-916X
Louis Martin-MonierDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Mikhail Y ShalaginovDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Tian GuDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID https://orcid.org/0000-0003-3989-6927
Juejun HuDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metasurfaces, which consist of arrays of ultrathin planar nanostructures (also known as "meta-atoms"), offer immense potential for use in high-performance optical devices through the precise manipulation of electromagnetic waves with subwavelength spatial resolution. However, designing meta-atom structures that simultaneously meet multiple functional requirements (e.g., for multiband or multiangle operation) is an arduous task that poses a significant design burden. Therefore, it is essential to establish a robust method for producing intricate meta-atom structures as functional devices. To address this issue, we developed a rapid construction method for a multifunctional and fabrication-friendly meta-atom library using deep neural networks coupled with a meta-atom selector that accounts for realistic fabrication constraints. To validate the proposed method, we successfully applied the approach to experimentally demonstrate a dual-band metasurface collimator based on complex free-form meta-atoms. Our results qualify the proposed method as an efficient and reliable solution for designing complex meta-atom structures in high-performance optical device implementations.

Indexed as

deep learningfabrication tolerancemetasurfacemultibandpredictive neural network

Identifiers

PMID39633861
PMCPMC11501907

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.