Evidence map›Paper›PMID 42204146›Full record

ArticleNature communications2026

Ultra-high density perovskite nanowire array memristor-based multi-layer perceptron.

Swapnadeep Poddar, Biswadeep Khan, Shivam Kumar, Jinghao Li, Yucheng Ding, Zhesi Chen, Kaichen Wang, Chenxi Jin, Jinchen Wei, Zhirong Peng and 4 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. 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

14 authors.

Swapnadeep Poddar *Department of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Biswadeep Khan *Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Shivam KumarDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Jinghao LiDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Yucheng DingDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-3808-8437
Zhesi ChenDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Kaichen WangDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Chenxi JinDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Jinchen WeiDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Zhirong PengDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
On-Mongkol JaesiriDepartment of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Mansun ChanDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
Julie L SemmelhackDepartment of Life Science, the Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China. jsemmelhack@ust.hk.ORCID http://orcid.org/0000-0002-8154-264X
Zhiyong FanDepartment of Electronic & Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China. eezfan@ust.hk.ORCID http://orcid.org/0000-0002-5397-0129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Passive memristor crossbars present a high-density and low-power platform for neuromorphic computing. Although perovskite memristors present advantages such as re-configurability between electrochemical metallization and valence change mechanisms compared to traditional oxides, they face issues with uniformity, variability, and filamentary instability, which impede their large-scale integration. This work demonstrates that these limitations are overcome by a three-dimensional perovskite nanowire array architecture, where a precisely engineered ITO barrier is instrumental in maintaining controlled, analog conductance modulation. The resulting memristors exhibit 139 non-overlapping Ohmic conduction states within an optimal analog range (10-100 µS), long retention ( > 10⁶ s), endurance exceeding 4 × 10⁵ cycles, an asymmetric non-linearity factor of 0.3, and minimal variability (device-to-device <5%, cycle-to-cycle <1.5%). The memristors are further integrated into a 64 × 64 crossbar array and utilized in developing a fully integrated multi-layer perceptron for zebrafish head and jaw movement analysis, thereby establishing a scalable pathway for robust perovskite-based neuromorphic hardware.

Identifiers

PMID42204146
PMCPMC13303867

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Registered trials

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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.