Evidence map›Paper›PMID 40140586›Full record

ArticleNature2025

Synaptic and neural behaviours in a standard silicon transistor.

Sebastian Pazos, Kaichen Zhu, Marco A Villena, Osamah Alharbi, Wenwen Zheng, Yaqing Shen, Yue Yuan, Yue Ping, Mario Lanza

Abstract read
In one paragraph

Article in Nature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Ultralow-Energy Analog Reservoir Computing via Reconfigurable 2-Transistor Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  2. Article
  3. Article
  4. Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  5. Neuromorphic In-Memory Computing for Marine Visual-Auditory Perception.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Ferroelectric Devices for In-Memory and In-Sensor Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

9 authors.

Sebastian PazosMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID http://orcid.org/0000-0002-7354-4530
Kaichen ZhuMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Marco A VillenaMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Osamah AlharbiMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Wenwen ZhengMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Yaqing ShenMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Yue YuanMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID http://orcid.org/0000-0002-7249-0411
Yue PingMaterials Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Mario LanzaDepartment of Materials Science and Engineering, National University of Singapore, Singapore, Singapore. mlanza@nus.edu.sg.ORCID http://orcid.org/0000-0003-4756-8632

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hardware implementations of artificial neural networks (ANNs)-the most advanced of which are made of millions of electronic neurons interconnected by hundreds of millions of electronic synapses-have achieved higher energy efficiency than classical computers in some small-scale data-intensive computing tasks

Identifiers

PMID40140586
PMCPMC11964925

What OpenQuestion holds

Textmetadata
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.