Measurement of feature roughness is complicated by the confounding noise inherent in SEM images. Edge detection typically requires image filtering to be reliable, but such filtering inevitably alters the roughness that one is trying to measure. Thus, there is a need for an edge detection approach that reliably detects edges in very noisy SEM images without the use of image filtering. The analytical linescan model (ALM) accomplishes this goal by using a physical model for linescan generation to constrain the possible shape of a linescan. Inverting a calibrated model allows edge positions to be estimated with very low sensitivity to noise. The ALM was used to detect edges for the application of roughness measurements and shown to provide superior results compared to conventional methods that employ image filtering.

Date Published: 28 March 2017
PDF: 8 pages
Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 101451R (28 March 2017); doi: 10.1117/12.2258631

Published in SPIE Proceedings Vol. 10145:
Metrology, Inspection, and Process Control for Microlithography XXXI
Martha I. Sanchez, Editor(s)