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- W3131679170 abstract "Although line edge and line width roughness (LER/LWR) have been key metrology challenges over the last 15 years, the advent of extreme-ultraviolet lithography (EUV) has increased the importance of its measurement and control. Lithographically printed features are now small enough that randomness in resist chemistry and in EUV photon during exposure results in noise in the patterned resist. This not only affects LER/LWR, but also defect density, including missing holes, shifted features, bridged lines and holes, and line shorts, among others. Well before these stochastic induced roughness variations, there have existed various techniques to analyze roughness. These include power spectral density algorithms, methods to account for instrument bias in the data, identify and filter noise, and specify measurement uncertainty. In this work, analysis methods to evaluate LER and LWR spatial wavelengths, including partitioning and filtering out instrument errors, such as noise and probe effects are presented. Our approach is based on wavelet-transform multiresolution analysis. One of the key advantages of wavelet transform over other signal processing techniques are its spatial-frequency localization and multi-scale view of the components of a profile or surface. This allows decomposing the data into different bands based on specific cutoffs and evaluating different approximations and surface-details at each cutoff band. A priori noise and probe information are used to determine and remove instrument-effects from the data, before calculating the unbiased roughness. The strength of this approach is that is it targeted only to specific spatial wavelengths that are associated with instrument noise or artifacts." @default.
- W3131679170 created "2021-03-01" @default.
- W3131679170 creator A5046451500 @default.
- W3131679170 date "2021-04-01" @default.
- W3131679170 modified "2023-09-27" @default.
- W3131679170 title "Spectral analysis of line edge and line width roughness using wavelets" @default.
- W3131679170 doi "https://doi.org/10.1117/12.2584649" @default.
- W3131679170 hasPublicationYear "2021" @default.
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