Data-driven approaches to optical patterned defect detection

Published

September 5, 2019

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

Pub Type

Journals

Download Paper

https://doi.org/10.1364/OSAC.2.002683

Keywords

Metrology, Defect understanding, Three-dimensional microscopy

Citation

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 October 6, 2025)