Evidence map›Paper›PMID 37893362›Full record

ArticleMicromachines2023

Chiral Metasurface Multifocal Lens in the Terahertz Band Based on Deep Learning.

Jingjing Wang, Sixue Chen, Yihang Qiu, Xiaoying Chen, Jian Shen, Chaoyang Li

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024
    Review
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

6 authors.

Jingjing WangSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.
Sixue ChenSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.
Yihang QiuSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.
Xiaoying ChenSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.
Jian ShenSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.
Chaoyang LiSchool of Electronic Science and Technology, Hainan University, Haikou 570228, China.

Funding

jianshen (2019RC054)
6 · The paper itself

Abstract

Chiral metasurfaces have garnered significant interest as an emerging field of metamaterials, primarily due to their exceptional capability to manipulate phase distributions at interfaces. However, the on-demand design of chiral metasurface structures remains a challenging task. To address this challenge, this paper introduces a deep learning-based network model for rapid calculation of chiral metasurface structure parameters. The network achieves a mean absolute error (MAE) of 0.025 and enables the design of chiral metasurface structures with a circular dichroism (CD) of 0.41 at a frequency of 1.169 THz. By changing the phase of the chiral metasurface, it is possible to produce not only a monofocal lens but also a multifocal lens. Well-designed chiral metasurface lenses allow us to control the number and position of focal points of the light field. This chiral metasurface, designed using deep learning, demonstrates great multifocal focus characteristics and holds great potential for a wide range of applications in sensing and holography.

Indexed as

chiral metasurfacecircular dichroismdeep learningmonofocal lensmultifocal lens

Identifiers

PMID37893362
PMCPMC10608832

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.