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To understand extreme ultraviolet (EUV) lithography performance of various materials (resists, underlayers etc) or processes (bake, development etc.) in terms of process window (PW) and defectivity, we typically use e-beam based tools (e.g., CDSEM) or optical inspection and defect reviews. The optical inspections can scan large areas quickly to pick up potential defects but give little information about the defect’s morphology. The e-beam inspections provide us with metrology information (CD, PW etc.) and detailed defect characteristics, but is very slow. To connect this gap, i.e., to be able to make high-level projections about process window variations and probable defectivity while scanning small area quickly, we need an intermediate analysis methodology bridging optical inspection and CDSEM analysis. With this objective, we present a new data analysis methodology for understanding process variations and probabilities of developing defects, by performing statistical analysis of the local CD variations for line/spaces patterned using EUV lithography. The local CDs obtained from a CDSEM image are assumed to follow the normal distribution curve. The deviations from the distribution i.e., the outlier local CD data, represent potential bridge and break defects and can help identify the probabilities of obtaining these defects for a process, material, condition etc. The outlier counts are obtained by performing statistical hypothesis testing (e.g., generalized extreme studentized deviate test) of the local CDs. Additional metrics such as p-value of the Shapiro-Wilk hypothesis test for local CD distribution are also measured to quantify the degree of normality of the distribution. Using these metrics, we compared different resists, underlayers and L/S pitches to demonstrate the novel utility of this data analysis method in understanding process variations and finding probable defects. We also demonstrate the validity of this analysis method by correlating the obtained outlier count with the standardized line roughness measurements and defectivity counts.
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