We propose here an alternative path to investigate and discriminate the root causes of LWR using only wafer data. It is based on Local Critical Dimension Uniformity (LCDU) decomposition , a methodology used to identify and quantify the individual LCDU contributors. The decomposition approach requires a smart sampling of the wafer print, in which an array of contact hole is measured in different dies multiple times. For such an approach to be successful, it is critical to ensure that the measurement locations are individually identified. Hence, it is necessary to anchor the metrology to a reference feature. A linear nested model  is then used to quantify the three main variability components (mask, shot noise, and metrology). This approach allows to sample thousands of features at mask, a task that would not be practically achievable through direct mask measurements.
In this work, LWR decomposition is implemented for the first time. To this aim, 18nm lines at 36nm pitch, printed by EUV lithography, were used. We specifically worked with a pattern including programmed defects, used as anchoring features for the metrology. In order to limit the impact of the metrology noise, expected to be higher for lines as compared to CH, we sampled over 8000 anchored measurements per image (in the CH case, only 81 measurements per image were needed). The LWR decomposition results indicated the dominance of the metrology noise, as expected. In addition, the mask contribution was observed to be less relevant that the shot noise.
To verify the accuracy of the LWR decomposition results, Power Spectral Density (PSD) analysis on wafer and mask SEM images was used. The metrology noise contribution was removed at both mask and wafer level using an un-biasing normalization of the PSD curves . The comparison with the PSD analysis confirmed the feasibility of LWR decomposition, opening the way to a more effective diagnostic technique for roughness and stochastics.