Evidence map›Paper›PMID 33163934›Full record

ReviewiScience2020

Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications.

Tae-Jun Ko, Hao Li, Sohrab Alex Mofid, Changhyeon Yoo, Emmanuel Okogbue, Sang Sub Han, Mashiyat Sumaiya Shawkat, Adithi Krishnaprasad, Molla Manjurul Islam, Durjoy Dev and 5 more

Erratum issuedAbstract readReview
In one paragraph

Review in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Neuromorphic Photoresponse in Ultrathin SnSACS applied materials & interfaces · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Atomically thin bioelectronics.Nature reviews. Materials · 2024
    Article
  7. Advances and Applications of Oxidized van der Waals Transition Metal Dichalcogenides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Graphene-based RRAM devices for neural computing.Frontiers in neuroscience · 2023
    Review
  13. Article
  14. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Tae-Jun KoNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Hao LiNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Sohrab Alex MofidNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Changhyeon YooNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Emmanuel OkogbueNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Sang Sub HanNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Mashiyat Sumaiya ShawkatNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Adithi KrishnaprasadNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Molla Manjurul IslamNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Durjoy DevNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Yongjun ShinDepartment of Materials Science and Engineering, Seoul National University, Seoul, 08826, South Korea.
Kyu Hwan OhDepartment of Materials Science and Engineering, Seoul National University, Seoul, 08826, South Korea.
Gwan-Hyoung LeeDepartment of Materials Science and Engineering, Seoul National University, Seoul, 08826, South Korea.
Tania RoyNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.
Yeonwoong JungNanoScience Technology Center, University of Central Florida, Orlando, FL 32826, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-dimensional (2D) layered materials and their heterostructures have recently been recognized as promising building blocks for futuristic brain-like neuromorphic computing devices. They exhibit unique properties such as near-atomic thickness, dangling-bond-free surfaces, high mechanical robustness, and electrical/optical tunability. Such attributes unattainable with traditional electronic materials are particularly promising for high-performance artificial neurons and synapses, enabling energy-efficient operation, high integration density, and excellent scalability. In this review, diverse 2D materials explored for neuromorphic applications, including graphene, transition metal dichalcogenides, hexagonal boron nitride, and black phosphorous, are comprehensively overviewed. Their promise for neuromorphic applications are fully discussed in terms of material property suitability and device operation principles. Furthermore, up-to-date demonstrations of neuromorphic devices based on 2D materials or their heterostructures are presented. Lastly, the challenges associated with the successful implementation of 2D materials into large-scale devices and their material quality control will be outlined along with the future prospect of these emergent materials.

Indexed as

Electrical PropertyElectronic EngineeringMaterials Science

Identifiers

PMID33163934
PMCPMC7600392

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.