Evidence map›Paper›PMID 40252021›Full record

ArticleACS applied materials & interfaces2025

Benchmarking Stochasticity behind Reproducibility: Denoising Strategies in Ta

Anna Nyáry, Zoltán Balogh, Botond Sánta, György Lázár, Nadia Jimenez Olalla, Juerg Leuthold, Miklós Csontos, András Halbritter

Abstract read
In one paragraph

Article in ACS applied materials & interfaces, 2025. 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

8 authors.

Anna NyáryDepartment of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.ORCID 0009-0004-7793-0940
Zoltán BaloghDepartment of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.ORCID 0000-0002-9580-361X
Botond SántaDepartment of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.
György LázárDepartment of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.
Nadia Jimenez OlallaInstitute of Electromagnetic Fields, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.
Juerg LeutholdInstitute of Electromagnetic Fields, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.ORCID 0000-0003-0111-8169
Miklós CsontosInstitute of Electromagnetic Fields, ETH Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.ORCID 0000-0002-2766-6860
András HalbritterDepartment of Physics, Institute of Physics, Budapest University of Technology and Economics, Muegyetem rkp. 3, H-1111 Budapest, Hungary.ORCID 0000-0003-4837-9745

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reproducibility, endurance, driftless data retention, and fine resolution of the programmable conductance weights are key technological requirements against memristive artificial synapses in neural network applications. However, the inherent fluctuations in the active volume impose severe constraints on the weight resolution. In order to understand and push these limits, a comprehensive noise benchmarking and noise reduction protocol is introduced. Our approach goes beyond the measurement of steady-state readout noise levels and tracks the voltage-dependent noise characteristics all along the resistive switching

Indexed as

1/f-type noisedenoising strategiesmemristornoise tuningresistive switching devicesvoltage-dependent noise

Identifiers

PMID40252021
PMCPMC12051165

What OpenQuestion holds

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LicenceCC BY
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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.