Evidence map›Paper›PMID 39591053›Full record

ArticleNanomaterials (Basel, Switzerland)2024

Metasurface-Based Image Classification Using Diffractive Deep Neural Network.

Kaiyang Cheng, Cong Deng, Fengyu Ye, Hongqiang Li, Fei Shen, Yuancheng Fan, Yubin Gong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2024. 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. Review
  2. 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

7 authors.

Kaiyang ChengInternational School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.ORCID 0000-0003-1652-4338
Cong DengInternational School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.
Fengyu YeInternational School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.
Hongqiang LiCollege of Electronic and Information Engineering, Tongji University, Shanghai 200092, China.
Fei ShenInternational School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.
Yuancheng FanKey Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology and School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an 710129, China.ORCID 0000-0002-7919-4148
Yubin GongInternational School of Microelectronics, Dongguan University of Technology, Dongguan 523808, China.

Funding

Guangdong Basic and Applied Basic Research Foundation (2021A1515110733)，Dongguan Science and Technology Commissioner Project (20231800500212)，Guangdong Provincial Key Laboratory Project (High-Performance Integrated Circuits and Systems Laboratory) No.2023KSYS003
6 · The paper itself

Abstract

The computer-assisted inverse design of photonic computing, especially by leveraging artificial intelligence algorithms, offers great convenience to accelerate the speed of development and improve calculation accuracy. However, traditional thickness-based modulation methods are hindered by large volume and difficult fabrication process, making it hard to meet the data-driven requirements of flexible light modulation. Here, we propose a diffractive deep neural network (D

Indexed as

diffractive deep neural networkimage classificationmetasurfaces

Identifiers

PMID39591053
PMCPMC11597900

What OpenQuestion holds

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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.