Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Resist surface investigations for reduction of Line-Edge-Roughness in Top Surface Imaging technology
Microelectronic Engineering
Microelectronic Engineering
Microelectronic Engineering - Proceedings of the 29th international conference on micro and nano engineering
Line edge roughness: experimental results related to a two-parameter model
Microelectronic Engineering - Proceedings of the 29th international conference on micro and nano engineering
Impact of line edge roughness on the resistivity of nanometer-scale interconnects
Microelectronic Engineering - Proceedings of the European workshop on materials for advanced metallization 2004
Line edge roughness detection using deep UV light scatterometry
Microelectronic Engineering
Determining the impact of statistical fluctuations on resist line edge roughness
Microelectronic Engineering
A review of line edge roughness and surface nanotexture resulting from patterning processes
Microelectronic Engineering
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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.