Data-driven approaches to optical patterned defect detection.

ABSTRACT: Computer vision and classification methods have become increasingly wide-spread in recent years due to ever-increasing access to computation power. Advances in semiconductor devices are the basis for this growth, but few publications have probed the benefits of data-driven methods for improving a critical component of semiconductor manufacturing, the detection and inspection of defects for such devices. As defects become smaller, intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection. These images are formed from the simulated scattering of realistic geometries with an

SUBMITTER: Henn MA 

PROVIDER: S-EPMC6902446 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature