30 March 2018 Setting up a proper power spectral density (PSD) and autocorrelation analysis for material and process characterization
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Abstract
Power spectral density (PSD) analysis is playing more and more a critical role in the understanding of line-edge roughness (LER) and linewidth roughness (LWR) in a variety of applications across the industry. It is an essential step to get an unbiased LWR estimate, as well as an extremely useful tool for process and material characterization. However, PSD estimate can be affected by both random to systematic artifacts caused by image acquisition and measurement settings, which could irremediably alter its information content. In this paper, we report on the impact of various setting parameters (smoothing image processing filters, pixel size, and SEM noise levels) on the PSD estimate. We discuss also the use of PSD analysis tool in a variety of cases. Looking beyond the basic roughness estimate, we use PSD and autocorrelation analysis to characterize resist blur[1], as well as low and high frequency roughness contents and we apply this technique to guide the EUV material stack selection. Our results clearly indicate that, if properly used, PSD methodology is a very sensitive tool to investigate material and process variations
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vito Rutigliani, Vito Rutigliani, Gian Francesco Lorusso, Gian Francesco Lorusso, Danilo De Simone, Danilo De Simone, Frederic Lazzarino, Frederic Lazzarino, Gijsbert Rispens , Gijsbert Rispens , George Papavieros, George Papavieros, Evangelos Gogolides, Evangelos Gogolides, Vassilios Constantoudis, Vassilios Constantoudis, Chris A. Mack, Chris A. Mack, } "Setting up a proper power spectral density (PSD) and autocorrelation analysis for material and process characterization", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851K (30 March 2018); doi: 10.1117/12.2297264; https://doi.org/10.1117/12.2297264
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