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This paper will first discuss alternative techniques and methodologies for detection of lithography-related defects, such as scumming and microbridging. These strategies will then be used to gain a better understanding of the effects of material property changes, process partitioning, and hardware improvements, ultimately correlating them directly with electrical yield detractors .
In this paper, we review key defectivity learning required to enable 7nm node and beyond technology. We will describe ongoing progress in addressing these challenges through track-based processes (coating, developer, baking), highlighting the limitations of common defect detection strategies and outlining methodologies necessary for accurate characterization and mitigation of blanket defectivity in EUV patterning stacks. We will further discuss defects related to pattern collapse and thinning of underlayer films.
Process assumptions (PAs) typically document the requirements between a current layer and a prior layer.2 With single exposure layers the process assumption for overlay error of a current layer to a prior layer could be set knowing an exposure tool’s on product overlay capability, error sources for the layers involved, maximum acceptable rework rate and yield-loss the fab was able to accept. For a current layer that minimizes back to a prior layer that was exposed with multiple exposures, there are new sources of error to understand and consider.3 Understanding how these process assumptions relate to the capability of simple single layer to single layer overlay is desirable. This paper takes a new approach to calculating these relationships by using image-placement error and population statistics. This is different than former methods discussed in the literature that look at “2nd order” overlay calculations. We show that base single layer to single layer process capability needs to be tighter than process assumption of a current layer minimizing back to multiple prior layers with the specific amount of tightening directly related to the mean overlay error between multipatterned layers. Because of this, mean overlay specifications have to be set appropriately at prior layers to match process assumptions. As an example, if a contact layer is split into two exposures, the mean translation error between the two exposures needs to be minimized for good metal to contact overlay. This paper will describe the exact controls needed based on the new statistical understanding.
Setting ground-rules based on overlay PAs that are correctly determined, using the image placement and population statistics, is critical. Without the proper statistical understanding, it can be concluded that single layer to single layer overlay capability cannot support a technology using multiple exposures, resulting in increased die areas as ground-rules are relaxed for 2nd order calculations incorrectly applied to the problem. Of course, the opposite is true if control of the mean overlay error of prior layers cannot be adequately controlled.
Through statistical analysis, we show that grouped overlay metrology of multiple exposures underestimates the true overlay error. This is due to the point-by-point averaging of layers that have been split into multiple exposures. Fortunately, the ratio between metrology and true overlay can be exactly calculated.
During SAQP process development, the challenges in conventional in-line metrology techniques start to surface. For instance, critical-dimension scanning electron microscopy (CDSEM) is commonly the first choice for CD and pitch variation control. However, it is found that the high aspect ratio at mandrel level processes and the trench variations after etch prevent the tool from extracting the true bottom edges of the structure in order to report the position shift. On the other hand, while the complex shape and variations can be captured with scatterometry, or optical CD (OCD), the asymmetric features, such as pitch walk, show low sensitivity with strong correlations in scatterometry. X-ray diffraction (XRD) is known to provide useful direct measurements of the pitch walk in crystalline arrays, yet the data analysis is influenced by the incoming geometry and must be used carefully.
A successful implementation of SAQP process control for yield improvement requires the metrology issues to be addressed. By optimizing the measurement parameters and beam configurations, CDSEM measurements distinguish each of the spaces corresponding to the upstream mandrel processes and report their CDs separately to feed back to the process team for the next development cycle. We also utilize the unique capability in scatterometry to measure the structure details in-line and implement a “predictive” process control, which shows a good correlation between the “predictive” measurement and the cross-sections from our design of experiments (DOE). The ability to measure the pitch walk in scatterometry was also demonstrated. This work also explored the frontier of in-line XRD capability by enabling an automatic RSM fitting on tool to output pitch walk values. With these advances in metrology development, we are able to demonstrate the impacts of in-line monitoring in the SAQP process, to shorten the patterning development learning cycle to improve the yield.
Overlay improvement roadmap: strategies for scanner control and product disposition for 5-nm overlay
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