Evidence map›Paper›PMID 39066077›Full record

ArticleSensors (Basel, Switzerland)2024

Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging.

Mona Knoblich, Mohammad Al Ktash, Frank Wackenhut, Tim Englert, Jan Stiedl, Hilmar Wittel, Simon Green, Timo Jacob, Barbara Boldrini, Edwin Ostertag and 2 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mona KnoblichCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0002-9459-0640
Mohammad Al KtashCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0002-6713-5669
Frank WackenhutCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0001-6554-6600
Tim EnglertRobert Bosch GmbH, Automotive Electronics, Tübingerstraße 123, 72762 Reutlingen, Germany.ORCID 0000-0001-8622-917X
Jan StiedlRobert Bosch GmbH, Automotive Electronics, Tübingerstraße 123, 72762 Reutlingen, Germany.
Hilmar WittelCenter of Physics, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Simon GreenRobert Bosch GmbH, Automotive Electronics, Tübingerstraße 123, 72762 Reutlingen, Germany.
Timo JacobInstitute of Electrochemistry, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany.ORCID 0000-0001-7777-2306
Barbara BoldriniCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0003-1357-8453
Edwin OstertagCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0003-0492-0544
Karsten RebnerCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0002-3577-2903
Marc BrechtCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.ORCID 0000-0001-5537-1448

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with

Indexed as

cleanlinesselectrical copperhyperspectral imagingpartial least squares regressionprincipal component analysispushbroomsolderingUV spectroscopy

Identifiers

PMID39066077
PMCPMC11281087

What OpenQuestion holds

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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.