Evidence map›Paper›PMID 42471312›Full record

ArticleNature communications2026

Wavelength-encoded neuromorphic inference enabled by microcavity MoS

Xiang Chen, Kangjian Di, Fuhao Yu, Jiashu Li, Lixiang Sun, Shengdi Chen, Silin Chen, Andy Liu, Xiang Peng, Shitong Zhu and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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

18 authors.

Xiang Chen *School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China.
Kangjian Di *School of Integrated Circuits, Nanjing University, Suzhou, China.
Fuhao Yu *Key Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Ministry of Education, Nanjing, China.
Jiashu LiSchool of Integrated Circuits, Nanjing University, Suzhou, China.
Lixiang SunSchool of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China.
Shengdi ChenSchool of Integrated Circuits, Nanjing University, Suzhou, China.
Silin ChenSchool of Integrated Circuits, Nanjing University, Suzhou, China.ORCID http://orcid.org/0009-0006-7170-367X
Andy LiuSchool of Integrated Circuits, Nanjing University, Suzhou, China.
Xiang PengSchool of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China.
Shitong ZhuNanjing Extremo Technology Co. Ltd., ., Nanjing, China.
Taotao LiSchool of Integrated Circuits, Nanjing University, Suzhou, China.ORCID http://orcid.org/0000-0002-2815-3856
Wei JiangKey Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Ministry of Education, Nanjing, China.ORCID http://orcid.org/0000-0003-0142-4465
Xuping ZhangKey Laboratory of Intelligent Optical Sensing and Manipulation, Nanjing University, Ministry of Education, Nanjing, China.
Yi ShiSchool of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China.ORCID http://orcid.org/0000-0002-1082-0315
Xinran WangSchool of Integrated Circuits, Nanjing University, Suzhou, China.ORCID http://orcid.org/0000-0002-3975-1667
Li GaoSchool of Science, Jiangsu Key Laboratory of Quantum Computing Science and Devices, Nanjing University of Posts and Telecommunications, Nanjing, China. iamlgao@njupt.edu.cn.ORCID http://orcid.org/0000-0003-2465-5784
Zehua HuSchool of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China. zehuahu@nju.edu.cn.ORCID http://orcid.org/0000-0002-1185-2992
Ningmu ZouSchool of Integrated Circuits, Nanjing University, Suzhou, China. nzou@nju.edu.cn.ORCID http://orcid.org/0000-0003-2505-4139

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62341408
6 · The paper itself

Abstract

Biological vision systems tightly couple spectral sensing with temporal integration to extract task-relevant information with minimal data movement. In contrast, conventional optoelectronic vision hardware typically separates photodetection from electronic computation, incurring substantial latency and energy costs. Optical neural networks can alleviate this bottleneck, but many implementations are difficult to scale. Here, we propose a bio-inspired optoelectronic inference architecture based on a Fabry-Perot microcavity-integrated MoS

Identifiers

PMID42471312
PMCPMC13493887

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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.