Evidence map›Paper›PMID 42483297›Full record

ReviewScience and technology of advanced materials2026

Kesterite-based optoelectronic synaptic memristors: a mini-review on material design and neuromorphic application.

Fengxia Yang, Hao Sun, Xiaofei Dong, Xiang Zhang, Jiangtao Chen, Xuqiang Zhang, Yun Zhao, Yan Li

Abstract readReview
In one paragraph

Review in Science and technology of advanced materials, 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

8 authors.

Fengxia YangCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Hao SunCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Xiaofei DongCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Xiang ZhangCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Jiangtao ChenCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Xuqiang ZhangCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Yun ZhaoCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.
Yan LiCollege of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of big data and artificial intelligence, the conventional Von Neumann architecture faces increasingly prominent issues of separation of storage and computing as well as high energy consumption, which severely hinder the progress of next-generation intelligent computing. Memristors, serving as fundamental components that enable in-memory computing and brain-inspired neuromorphic computing, naturally possess intrinsic benefits, namely high-density storage, smoothly adjustable conductance, and low power consumption, thus emerging as promising solutions to break through this bottleneck. Among various resistive switching materials, kesterite-based chalcogenides have become the promising choice for optoelectronic synaptic memristors due to their superior optoelectronic characteristics, rich elemental availability, green nature, and economic viability. This work concisely summarizes the research progress of kesterite-based optoelectronic synaptic memristors in terms of resistive switching performance optimization, synaptic behavior simulation, and neuromorphic applications, focusing on the effects of resistive switching layer thickness, elemental composition, heterojunction and composite structures, electrode engineering, as well as testing environment and operating modes. Furthermore, it discusses the current challenges and future development trends, providing an important guideline for designing, optimizing, and practically deploying high-performance neuromorphic devices based on kesterite.

Indexed as

artificial synapseKesteritememristorneuromorphic applicationperformance regulation

Identifiers

PMID42483297
PMCPMC13386588

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.