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

## TL;DR

This paper introduces an autonomous system for inspecting thin-film coatings on large substrates using a robotic manipulator and multi-sensor array to improve accuracy and scalability.

## Contribution

The novel constraint manifold for robotic control and scalable multi-sensor array for large-area inspection are the key innovations.

## Key findings

- The manipulator can perform required motions while adhering to movement constraints.
- The sensor array achieves thickness measurement errors of ≤2.1% compared to a commercial reflectometer.
- The system can dynamically detect angle variations >0.5° from the calibration point.

## 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 \documentclass[12pt]{minimal}
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\begin{document}$$>0.5^\circ$$\end{document} from the calibration point whilst monitoring the RMSE and R\documentclass[12pt]{minimal}
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\begin{document}$$^2$$\end{document} over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes.