Evidence map›Paper›PMID 41758685›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Recent Advances and Perspectives on Field-Effect Transistors for Artificial Visual Neuromorphic Systems.

Liu Yaqian, Lang Menghua, Xu Yihang, Zhang Manyu, Lei Chunkang, Lu Xiaozhou, Dou Yi, Wang Lingli, Jiang Liying, Hu Yuanyuan and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. 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. 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

12 authors.

Liu YaqianSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Lang MenghuaSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Xu YihangSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Zhang ManyuSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Lei ChunkangSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Lu XiaozhouSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Dou YiSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Wang LingliSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Jiang LiyingSchool of Electronics and Information, Zhengzhou University of Light Industry, Zhengzhou, China.
Hu YuanyuanChangsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, China.
Chen HuipengInstitute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, China.ORCID https://orcid.org/0000-0003-1706-3174
Jiang LangSchool of Chemical Engineering, Hebei University of Technology, Tianjin, China.ORCID https://orcid.org/0000-0003-3624-1618

Funding

China Postdoctoral Science Foundation 2025M770541Key Research and Development Program of Henan Province 241111220400Key Research Project of Department of Science and Technology in Henan Province 252102211098National Key R&D Program of China 2024YFE0102400National Natural Science Foundation of China 62404206National Natural Science Foundation of China T2225028National Natural Science Foundation of China U22A6002The Science and Technology Major project of Henan Province 231100220800The Science and Technology Major project of Henan Province 241100210400
6 · The paper itself

Abstract

The exponential growth of data has exposed the inherent bottlenecks of the von Neumann architecture-specifically its limited computational efficiency and high energy consumption-necessitating an urgent shift toward innovative hardware solutions. Biological perception systems, particularly the human visual system, serve as a premier model for highly integrated, energy-efficient, and multimodal processing, providing a critical blueprint for the future of intelligent computing. Field-effect transistors (FETs) have emerged as a leading platform for visual neuromorphic systems, leveraging their exceptional optoelectronic tunability, mechanical flexibility, and low-power operation. This review provides a comprehensive overview of FET-based visual neuromorphic systems, covering semiconductor material selection, fundamental device architectures, and governing operational principles. Then, the critical role of these devices in emulating biological visual functions is detailed. Finally, the prevailing technical challenges and future development prospects for FET-mediated perception are discussed. This work aims to provide essential insights into the design of the next generation of artificial visual neuromorphic systems and bio-inspired electronics.

Indexed as

artificial visual neuromorphic systemsfield effect transistorssynaptic transistorsvisual perception

Identifiers

PMID41758685
PMCPMC13067775

What OpenQuestion holds

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LicenceCC BY
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.