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Proceedings Paper

Comparing edge detection algorithms: their impact on unbiased roughness measurement precision and accuracy
Author(s): Chris A. Mack
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Paper Abstract

Background: Understanding line-edge and linewidth roughness in semiconductor patterning requires accurate, unbiased measurements where noise in the scanning electron microscope (SEM) image does not impact the measured roughness. This in turn requires edge detection algorithms with minimum sensitivity to SEM noise since unbiased roughness measurement does not allow the use of image filtering. Aim: There is a need to characterize and evaluate the noise sensitivity of edge detection algorithms used in SEM metrology. Approach: The noise floor of the roughness power spectral density will be used as a metric of noise sensitivity to compare three edge detection algorithms (derivative, threshold, and Fractilia Inverse Linescan Model (FILM)) using three sets of images (low-noise, mid-noise, and higher-noise cases). Results: The derivative edge detection algorithm performed poorly even on low-noise images. The threshold algorithm worked well only on the low-noise images. For all levels of noise in the images, the FILM algorithm performed well, and better than the threshold and derivative methods. Conclusions: An approach to unbiased roughness measurement that requires measurement of the noise floor without the use of image filtering requires an edge detection algorithm with inherently low noise sensitivity. The testing approach used here, comparing the noise floor level for different algorithms applied to the same images, is an effective way to evaluate the inherent noise sensitivity of edge detection algorithms.

Paper Details

Date Published: 20 March 2020
PDF: 12 pages
Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113250P (20 March 2020);
Show Author Affiliations
Chris A. Mack, Fractilia, LLC (United States)

Published in SPIE Proceedings Vol. 11325:
Metrology, Inspection, and Process Control for Microlithography XXXIV
Ofer Adan; John C. Robinson, Editor(s)

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