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Proceedings Paper

Applications of machine learning at the limits of form-dependent scattering for defect metrology
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Paper Abstract

Undetected patterning defects on semiconductor wafers can have severe consequences, both financially and technologically. Industry is challenged to find reliable and easy-to-implement methods for defect detection. In this paper we present robust machine learning techniques that can be applied to classify defect images. We demonstrate the basic principles of an algorithm that uses a convolutional neural network and discuss how such networks can be improved not only in their architecture but also tailored to the specific challenges of defect inspection through more specialized performance metrics. These advances may lead to more cost-efficient measurements by adjusting the decision threshold to minimize the number of wrong defect detections.

Paper Details

Date Published: 26 March 2019
PDF: 8 pages
Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109590Z (26 March 2019); doi: 10.1117/12.2517285
Show Author Affiliations
Mark-Alexander Henn, National Institute of Standards and Technology (United States)
Hui Zhou, National Institute of Standards and Technology (United States)
Richard M. Silver, National Institute of Standards and Technology (United States)
Bryan M. Barnes, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 10959:
Metrology, Inspection, and Process Control for Microlithography XXXIII
Vladimir A. Ukraintsev; Ofer Adan, Editor(s)

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