Setting up a proper power spectral density and autocorrelation analysis for material and process characterization

Abstract

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.