Abstract
A wavelet-based line-edge detection framework is presented that proves to be solely image-dependent. In this analysis, surfaces are considered as a combination of an underlying surface structure and a surface detail, corresponding to low-frequency and high-frequency features, respectively. Through the multi-scale analysis offered by wavelet decomposition, the underlying surface structure is extracted and used to define the line-edge searching region, which, in turn, helps characterize the line-edge roughness (LER), providing valuable information for the evaluation of device fabrication and performance. We focus on exploring the optimal wavelet decomposition, to better separate the underlying structure and the surface detail, using a number of metrics including the Shannon’s entropy, k-means clustering and the flatness factor. The impact of different wavelet functions and resolution levels on line-edge roughness characterization is discussed. An SEM image of a plane diffraction grating is studied to demonstrate the application of the proposed framework.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
