Evidence map›Paper›PMID 41633978›Full record

ArticleLight, science & applications2026

Anti-interference diffractive deep neural networks for multi-object recognition.

Zhiqi Huang, Yufei Liu, Nan Zhang, Zian Zhang, Qiming Liao, Cong He, Shendong Liu, Youhai Liu, Hongtao Wang, Xingdu Qiao and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Light, science & applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Zhiqi Huang *Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Yufei Liu *Beijing Key Laboratory of Metamaterials and Devices, Key Laboratory of Terahertz Optoelectronics of Ministry of Education, Capital Normal University, Department of Physics, Capital Normal University, Beijing, 100048, China.
Nan Zhang *Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China. nanzhang@bit.edu.cn.ORCID http://orcid.org/0009-0008-9165-4064
Zian ZhangBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Qiming LiaoBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Cong HeBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Shendong LiuBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Youhai LiuQiyuan Lab, Beijing, 100095, China.
Hongtao WangEngineering Product Development, Singapore University of Technology and Design, Singapore, 487372, Singapore.ORCID http://orcid.org/0000-0001-9736-2208
Xingdu QiaoDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Joel K W YangEngineering Product Development, Singapore University of Technology and Design, Singapore, 487372, Singapore.ORCID http://orcid.org/0000-0003-3301-1040
Yan ZhangBeijing Key Laboratory of Metamaterials and Devices, Key Laboratory of Terahertz Optoelectronics of Ministry of Education, Capital Normal University, Department of Physics, Capital Normal University, Beijing, 100048, China. yzhang@cnu.edu.cn.ORCID http://orcid.org/0000-0001-9235-6861
Lingling HuangBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China. huanglingling@bit.edu.cn.ORCID http://orcid.org/0000-0002-3647-2128
Yongtian WangBeijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.ORCID http://orcid.org/0000-0001-9422-0888

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62575023Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation) JQ24028
6 · The paper itself

Abstract

Optical neural networks (ONNs) are emerging as a promising neuromorphic computing paradigm for object recognition, offering unprecedented advantages in light-speed computation, ultra-low power consumption, and inherent parallelism. However, most of ONNs are only capable of performing simple object classification tasks. These tasks are typically constrained to single-object scenarios, which limits their practical applications in multi-object recognition tasks. Here, we propose an anti-interference diffractive deep neural network (AI D

Identifiers

PMID41633978
PMCPMC12868834

What OpenQuestion holds

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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.