Evidence map›Paper›PMID 40804306›Full record

ArticleNature communications2025

Highly efficient photonic convolver via lossless mode-division fan-in.

Shangsen Sun, Shiji Zhang, Bo Wu, Shan Jiang, Baiheng Zhao, Hailong Zhou, Jianji Dong, Xinliang Zhang

Abstract read
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Shangsen Sun *Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Shiji Zhang *Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.ORCID http://orcid.org/0009-0000-9380-5542
Bo WuWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Shan JiangWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Baiheng ZhaoWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Hailong ZhouWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.ORCID http://orcid.org/0000-0002-1878-4291
Jianji DongWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China. jjdong@mail.hust.edu.cn.ORCID http://orcid.org/0000-0002-1852-8650
Xinliang ZhangWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.ORCID http://orcid.org/0000-0001-8513-3328

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62425504National Natural Science Foundation of China (National Science Foundation of China) U21A20511
6 · The paper itself

Abstract

Optical neural networks (ONNs) leverage the parallelism and low-energy consumption of photonic signal processing to overcome the limitations of traditional electronic computing. Optics inherently enables fan-in and fan-out without the Resistor-Capacitor (RC) and Inductor-Capacitor (LC) delays of electrical interconnects. However, for single-mode photonic integrated circuits, reciprocity constraints introduce unavoidable loss during beam combining, hindering large-scale on-chip photonic fan-in. To overcome this challenge, we provide a photonic lossless mode-division fan-in solution for the convolution accelerators. Using inverse design, we developed a compact multimode photonic convolution accelerator (0.42 mm

Identifiers

PMID40804306
PMCPMC12350799

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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.