Comparing edge detection algorithms: their impact on unbiased roughness measurement precision and accuracy

Chris A. Mack

doi:10.1117/12.2551283

20 March 2020 Comparing edge detection algorithms: their impact on unbiased roughness measurement precision and accuracy

Chris A. Mack

Author Affiliations +

Proceedings Volume 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV; 113250P (2020) https://doi.org/10.1117/12.2551283
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

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.

Conference Presentation

Citation Download Citation

Chris A. Mack "Comparing edge detection algorithms: their impact on unbiased roughness measurement precision and accuracy", Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113250P (20 March 2020); https://doi.org/10.1117/12.2551283

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