Evidence map›Paper›PMID 36422434›Full record

ArticleMicromachines2022

Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.

Fernando Leonel Aguirre, Eszter Piros, Nico Kaiser, Tobias Vogel, Stephan Petzold, Jonas Gehrunger, Timo Oster, Christian Hochberger, Jordi Suñé, Lambert Alff and 1 more

Abstract read
In one paragraph

Article in Micromachines, 2022. 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

11 authors.

Fernando Leonel AguirreDepartament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.ORCID 0000-0001-7793-1194
Eszter PirosAdvanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Nico KaiserAdvanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Tobias VogelAdvanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Stephan PetzoldAdvanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Jonas GehrungerComputer Systems Group, Department of Electrical and Information Engineering, Technische Universität Darmstadt, 64289 Darmstadt, Germany.ORCID 0000-0002-0585-7893
Timo OsterIntegrated Electronic Systems, Department of Electrical and Information Engineering, Technische Universität Darmstadt, 64289 Darmstadt, Germany.ORCID 0000-0003-1501-2795
Christian HochbergerComputer Systems Group, Department of Electrical and Information Engineering, Technische Universität Darmstadt, 64289 Darmstadt, Germany.
Jordi SuñéDepartament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.ORCID 0000-0003-0108-4907
Lambert AlffAdvanced Thin Film Technology Division, Institute of Materials Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany.ORCID 0000-0001-8185-4275
Enrique MirandaDepartament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.ORCID 0000-0003-0470-5318

Funding

Deutsche Forschungsgemeinschaft 384682067Deutsche Forschungsgemeinschaft AL 560/13-2Deutsche Forschungsgemeinschaft AL 560/21-1ECSEL StorAIge-101007321ECSEL WAKeMeUP-783176European Union MEMQuD - 20FUN06Federal Ministry of Education and Research 16ESE0298Federal Ministry of Education and Research 16MEE0154Merck (Germany) Sustainability Hub
6 · The paper itself

Abstract

In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (

Indexed as

curve fittingdynamic memdiodememristorneural networksRRAM

Identifiers

PMID36422434
PMCPMC9698277

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.