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10 November 2018 Setting up a proper power spectral density and autocorrelation analysis for material and process characterization
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Power spectral density (PSD) analysis is playing a more critical role in the understanding of line-edge roughness 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 estimates can be affected by both random and systematic artifacts caused by image acquisition and measurement settings, which could irremediably alter its information content. 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 a PSD analysis tool in a variety of cases. Looking beyond the basic roughness estimate, we use PSD and autocorrelation analysis to characterize resist blur, as well as low and high frequency roughness contents, applying 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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1932-5150/2018/$25.00 © 2018 SPIE
Vito Rutigliani, Gian Francesco Lorusso, Danilo De Simone, Frederic Lazzarino, George Papavieros, Evangelos Gogolides, Vassilios Constantoudis, and Chris A. Mack "Setting up a proper power spectral density and autocorrelation analysis for material and process characterization," Journal of Micro/Nanolithography, MEMS, and MOEMS 17(4), 041016 (10 November 2018).
Received: 16 June 2018; Accepted: 15 October 2018; Published: 10 November 2018

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