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Data-driven approaches to optical patterned defect detection
Published
Author(s)
Mark-Alexander Henn, Hui Zhou, Bryan M. Barnes
Abstract
Computer vision and classification methods have become increasingly popular in recent years due to ever-increasing computation power. While advances in semiconductor devices are the basis for this growth, few publications have probed the benefits of data-driven methods for improving the defect detection and inspection for such devices. As defects become smaller, threshold-based approaches eventually fail to pick up differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) for image-based defect detection. These images are formed from the simulated scattering off realistic geometries with key defects taking into account line edge roughness, a known and challenging problem in semiconductor manufacturing. A fundamental CNN approach is shown to extend detectability to these smaller defects, even those that are more than 30 times smaller than the inspection wavelength.
Henn, M.
, Zhou, H.
and Barnes, B.
(2019),
Data-driven approaches to optical patterned defect detection, OSA Continuum, [online], https://doi.org/10.1364/OSAC.2.002683
(Accessed May 29, 2023)