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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.
Citation
OSA Continuum
Volume
2
Issue
9

Keywords

Metrology, Defect understanding, Three-dimensional microscopy
Created September 5, 2019, Updated January 27, 2020