Evidence map›Paper›PMID 41224992›Full record

ArticleScientific reports2025

Enhancing thin-film wafer inspection with a multi-sensor array and robot constraint maintenance.

Néstor Eduardo Sánchez-Arriaga, Ethan Canzini, Nathan John Espley-Plumb, Michael Farnsworth, Simon Pope, Adrian Leyland, Ashutosh Tiwari

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

7 authors.

Néstor Eduardo Sánchez-Arriaga *School of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK.
Ethan Canzini *School of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK. e.a.canzini@sheffield.ac.uk.
Nathan John Espley-Plumb *School of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK.
Michael FarnsworthSchool of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK.
Simon PopeSchool of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK.
Adrian LeylandSchool of Chemical, Material & Biological Engineering, University of Sheffield, Sheffield, UK.
Ashutosh TiwariSchool of Mechanical, Aerospace & Civil Engineering, University of Sheffield, Sheffield, UK.

Funding

Consejo Nacional de Ciencia y Tecnología (National Council of Science and Technology, Mexico) 1059795EPSRC ICASE Award with Airbus UK EP/S036180/1EPSRC ICASE Award with Airbus UK EP/T024607/1EPSRC ICASE Award with Siemens UK EP/V062123/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/S036180/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/T024607/1RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/V062123/1Royal Academy of Engineering RCSRF1718/5/4Royal Academy of Engineering RCSRF1718/5/41
6 · The paper itself

Abstract

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality; however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing, where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. It is demonstrated that the manipulator can perform required motions whilst adhering to movement constraints. It is further demonstrated that the sensor array can perform thickness measurements statically with an error of [Formula: see text] compared to a commercial reflectometer, and through the use of a manipulator can dynamically detect angle variations [Formula: see text] from the calibration point whilst monitoring the RMSE and R[Formula: see text] over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes.

Indexed as

Learned manifoldsMulti-sensor arrayRoboticsWafer inspection

Identifiers

PMID41224992
PMCPMC12612131

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.