Contrast-Based Assist Feature Optimization
By defining contrast as the maximum image-log-slope (ILS), we propose a novel method to optimize assist feature sites and locations. We present results that indicate that an ILS optimization at best focus provides enough information to arrive at a solution that improves the depth of focus of the design. Sub-resolution assist features (SRAF) are inserted using a rule-based approach that depends on the equivalent contrast of the original design. Later, an optical rule check (ORC) is performed to identify the regions in which the contrast of the main features is below a certain threshold. After such regions have been properly identified and selected, the neighboring edges are subjected to a sensitivity analysis that returns a contrast matrix which can be later compressed in a global contrast cost function. New positions of the assist feature edges are later evaluated and the assist features are modified accordingly. By following these steps, it is possible to alter: location, width and shape of the assist features in such a way that there is an overall improvement of the main feature contrast. A complete and integrated approach should be able to accept restrictions in the printability of assist features. In order to eliminate errors coming from the cross interactions between the globally optimized assist features and the original design, we incorporate a local clean up procedure that preserves the global validity of the current assist feature rule while improving the local behavior of the original edges. In this fashion, killer defects due to inter-rule dependencies are avoided.
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