Distribution measurement of line edge roughness using a noise correction based on height–height correlation function

BackgroundLine edge roughness (LER) is a critical performance metric in lithography. Current power-spectral-density-based methods, commonly used to evaluate roughness parameters assuming scale-limited self-affine fractal profiles, provide only an average LER value across multiple patterns.AimWe aim to perform noise-corrected (unbiased) LER measurements on individual patterns, thus enabling detailed characterization of their distribution across multiple patterns.ApproachWe applied our proposed height–height correlation function (HHCF)-based noise-correction technique by acquiring profiles from repeated measurements at identical locations and reducing noise via averaging. The average data were then fitted to a theoretical HHCF expression to separate the unbiased HHCF component from noise, and experimental validation was performed on Si line/space patterns with 50 nm linewidths.ResultsThe noise-reduced profiles exhibited sufficiently low noise to allow the extraction of unbiased HHCF curves and corresponding roughness parameters from each profile. Subsequent analysis of 1000 such profiles successfully characterized the LER distribution.ConclusionUsing the HHCF–based noise correction technique, we determined roughness parameters for each pattern, enabling detailed characterization of LER distributions. The LER distribution offers an efficient approach for evaluating mean LER, which remains a critical performance metric. In future work, applying this distribution could support the identification of outliers with exceptionally large LER values and thereby facilitate yield improvement and process optimization.