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

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.

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Publication Details

Journal
Scientific Reports
Published
2025-11-12
DOI
https://doi.org/10.1038/s41598-025-23640-5
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

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

A. Leyland, Michael Farnsworth, Simon Pope, Ashutosh Tiwari et al.
Scientific Reports
Soft Robotics and Applications
article

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

A. Leyland, Michael Farnsworth, Simon Pope, Ashutosh Tiwari, Néstor Eduardo Sánchez-Arriaga, Ethan Canzini, Nathan John Espley-Plumb
article en

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.

Scientific ReportsVol. 15(1)
University of Sheffield (GB), Airbus (United Kingdom) (GB)
UK Research and Innovation, Henry Royce Institute, Royal Academy of Engineering, Imperial College London, Consejo Nacional de Ciencia y Tecnología, Engineering and Physical Sciences Research Council, Airbus UK
Openalex Percentile: Top 67%
Soft Robotics and Applications
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