Evidence map›Paper›PMID 38937391›Full record

ReviewNano convergence2024

Two-dimensional material-based memristive devices for alternative computing.

Jey Panisilvam, Ha Young Lee, Sujeong Byun, Daniel Fan, Sejeong Kim

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nano convergence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

5 authors.

Jey PanisilvamDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia.
Ha Young LeeDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia.
Sujeong ByunDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia.
Daniel FanDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia.
Sejeong KimDepartment of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia. sejeong.kim@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.

Indexed as

2D materialsCrossbar arraysMemristorsNeuromorphic computingResistive switching

Identifiers

PMID38937391
PMCPMC11211314

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.