At 28nm technology node and below, hot spot prediction and process window control across production wafers have become increasingly critical to prevent hotspots from becoming yield-limiting defects. We previously established proof of concept for a systematic approach to identify the most critical pattern locations, i.e. hotspots, in a reticle layout by computational lithography and combining process window characteristics of these patterns with across-wafer process variation data to predict where hotspots may become yield impacting defects [1,2]. The current paper establishes the impact of micro-topography on a 28nm metal layer, and its correlation with hotspot best focus variations across a production chip layout. Detailed topography measurements are obtained from an offline tool, and pattern-dependent best focus (BF) shifts are determined from litho simulations that include mask-3D effects. We also establish hotspot metrology and defect verification by SEM image contour extraction and contour analysis. This enables detection of catastrophic defects as well as quantitative characterization of pattern variability, i.e. local and global CD uniformity, across a wafer to establish hotspot defect and variability maps. Finally, we combine defect prediction and verification capabilities for process monitoring by on-product, guided hotspot metrology, i.e. with sampling locations being determined from the defect prediction model and achieved prediction accuracy (capture rate) around 75%
The concept of the multi-source focus correlation method was presented in 2015 [1, 2]. A more accurate understanding of real on-product focus can be obtained by gathering information from different sectors: design, scanner short loop monitoring, scanner leveling, on-product focus and topography.
This work will show that chip topography can be predicted from reticle density and perimeter density data, including experimental proof. Different pixel sizes are used to perform the correlation in-line with the minimum resolution, correlation length of CMP effects and the spot size of the scanner level sensor. Potential applications of the topography determination will be evaluated, including optimizing scanner leveling by ignoring non-critical parts of the field, and without the need for time-consuming offline topography measurements.
At the 28nm technology node and below, hot spot prediction and process window control across production wafers have become increasingly critical. We establish proof off concept for ASML’s holistic lithography hot spot detection and defect monitoring flow, process window optimizer (PPWO), for a 228nm metal layer process. We demonstrate prediction and verification of defect occurrence on wafer that arise from focus variations exceeding process window margins of device hotspots. We also estimate the improvement potential if design aware scanner control was applied.
With continuing dimension shrinkage using the TWINSCAN NXT:1950i scanner on the 28nm node and beyond, the imaging depth of focus (DOF) becomes more critical. Focus budget breakdown studies [Ref 2, 5] show that even though the intrafield component stays the same, it becomes a larger relative percentage of the overall DOF. Process induced topography along with reduced Process Window can lead to yield limitations and defectivity issues on the wafer. In a previous paper, the feasibility of anticipating the scanner levelling measurements (Level Sensor, Agile and Topography) has been shown . This model, built using a multiple variable analysis (PLS: Partial Least Square regression) and GDS densities at different layers showed prediction capabilities of the scanner topography readings up to 0.78 Q² (the equivalent of R² for expected prediction). Using this model, care areas can be defined as parts of the field that cannot be seen nor corrected by the scanner, which can lead to local DOF shrinkage and printing issues. This paper will investigate the link between the care areas and the intrafield focus that can be seen at the wafer level, using offline topography measurements as a reference. Some improvements made on the model are also presented.
With continuing dimension shrinkage using the TWINSCAN NXT:1950i scanner on the 28nm node and beyond, the imaging depth of focus (DOF) becomes more critical. Focus budget breakdown studies [Ref 1, 5] show that even though the intrafield component stays the same this becomes a larger relative percentage of the overall DOF. Process induced topography along with reduced Process Window can lead to yield limitations and defectivity issues on the wafer. To improve focus margin, a study has been started to determine if some correlations between scanner levelling performance, product layout and topography can be observed. Both topography and levelling intrafield fingerprints show a large systematic component that seems to be product related. In particular, scanner levelling measurement maps present a lot of similarities with the layout of the product. The present paper investigates the possibility to model the level sensor’s measured height as a function of layer design densities or perimeter data of the product. As one component of the systematics from the level sensor measurements is process induced topography due to previous deposition, etching and CMP, several layer density parameters were extracted from the GDS’s. These were combined through a multiple variable analysis (PLS: Partial Least Square regression) to determine the weighting of each layer and each parameter. Current work shows very promising results using this methodology, with description quality up to 0.8 R2 and expected prediction quality up to 0.78 Q2. Since product layout drives some intrafield focus component it is also important to be able to assess intrafield focus uniformity from post processing. This has been done through a hyper dense focus map experiment which is presented in this paper.