Evidence map›Paper›PMID 34588444›Full record

ArticleNature communications2021

Reconfigurable Stochastic neurons based on tin oxide/MoS

Xiaodong Yan, Jiahui Ma, Tong Wu, Aoyang Zhang, Jiangbin Wu, Matthew Chin, Zhihan Zhang, Madan Dubey, Wei Wu, Mike Shuo-Wei Chen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Perovskite Neuromorphic Engine for Transformer Architectures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  2. Thermally Oxidized Memristor and 1T1R Integration for Selector Function and Low-Power Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  3. Article
  4. Review
  5. CMOS Single-Photon Avalanche Diode Circuits for Probabilistic Computing.IEEE journal on exploratory solid-state computational devices and circuits · 2024
    Article
  6. 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

12 authors.

Xiaodong Yan *Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0002-7737-6984
Jiahui Ma *Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Tong WuDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.ORCID 0000-0001-6018-0283
Aoyang ZhangMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Jiangbin WuMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0002-8751-7082
Matthew ChinSensors and Electron Devices Directorate, U.S. Army Research Laboratory, Adelphi, MD, 20723, USA.
Zhihan ZhangSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Madan DubeySensors and Electron Devices Directorate, U.S. Army Research Laboratory, Adelphi, MD, 20723, USA.
Wei WuMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.ORCID 0000-0001-6404-0317
Mike Shuo-Wei ChenMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Jing GuoDepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.ORCID 0000-0003-4009-3056
Han WangMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA. han.wang.4@usc.edu.ORCID 0000-0001-5121-3362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnO

Identifiers

PMID34588444
PMCPMC8481256

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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.