Evidence map›Paper›PMID 41271715›Full record

ArticleNature communications2025

High computational density nanophotonic media for machine learning inference.

Zhenyu Zhao, Yichen Pan, Jinlong Xiang, Yujia Zhang, An He, Yaotian Zhao, Youlve Chen, Yu He, Xinyuan Fang, Yikai Su and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

12 authors.

Zhenyu ZhaoState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0009-0007-9019-1195
Yichen PanState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0009-0005-8597-0704
Jinlong XiangState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0002-3047-3461
Yujia ZhangState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
An HeState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yaotian ZhaoState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Youlve ChenState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yu HeState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xinyuan FangSchool of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai, China. xinyuan.fang@usst.edu.cn.ORCID http://orcid.org/0000-0001-5904-2502
Yikai SuState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0002-1526-8187
Min GuSchool of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai, China.ORCID http://orcid.org/0000-0003-4078-253X
Xuhan GuoState Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China. guoxuhan@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-4891-0201

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62175151National Natural Science Foundation of China (National Science Foundation of China) 62341508
6 · The paper itself

Abstract

Efficient machine learning inference is essential for the rapid adoption of artificial intelligence (AI) across various domains. On-chip optical computing has emerged as a transformative solution due to its ultra-low power consumption, yet improving computational density remains challenging because of the difficulty of miniaturizing interference-based components. Here, we demonstrate fabrication-constrained scattering optical computing within nanophotonic media, enabled by fabrication-aware inverse design. This yields an ultra-compact optical neural architecture occupying 64 µm²-a three-order reduction compared to conventional optical neural networks. Our prototype achieves 86.7% accuracy on the Iris dataset, closely matching simulations. To further validate scalability, we train a larger 64-input design for optical character recognition using 8×8 handwritten digits, reaching 92.8% test accuracy. These results highlight the potential of nanophotonic media to perform large-scale tasks in ultra-small footprints, paving the way for dense, energy-efficient optical processors for next-generation AI.

Identifiers

PMID41271715
PMCPMC12638771

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.