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

Published

Author(s)

Mark Alexander Henn, Hui Zhou, Richard M. Silver, Bryan M. Barnes

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 optimize the number of wrongly classified no-defect images.
Proceedings Title
Metrology, Inspection, and Process Control for Microlithography XXXIII
Volume
10959
Conference Dates
February 24-March 1, 2019
Conference Location
San Jose, CA
Conference Title
SPIE Advanced Lithography 2019

Keywords

electromagnetic simulation, sensitivity and uncertainty evaluation, through-focus three- dimensional field

Citation

, M. , Zhou, H. , Silver, R. and Barnes, B. (2019), Applications of machine learning at the limits of form-dependent scattering for defect metrology, Metrology, Inspection, and Process Control for Microlithography XXXIII, San Jose, CA, [online], https://doi.org/10.1117/12.2517285 (Accessed May 20, 2024)

Issues

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Created March 26, 2019, Updated January 27, 2020