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Data-driven approaches to optical patterned defect detection



Mark-Alexander Henn, Hui Zhou, Bryan M. Barnes


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.
OSA Continuum


Metrology, Defect understanding, Three-dimensional microscopy


Henn, M. , Zhou, H. and Barnes, B. (2019), Data-driven approaches to optical patterned defect detection, OSA Continuum, [online], (Accessed July 23, 2024)


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Created September 5, 2019, Updated January 27, 2020