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In previous work1, we introduced a new technology called Flexible Mask Optimization (FMO) that was successfully used for localized OPC correction. OPC/RET techniques such as model-based assist feature and process-window-based OPC solvers have become essential for addressing critical patterning issues at 2× and lower technology nodes. With an FMO flow, critical patterns were identified, classified and corrected in localized areas only, using advanced techniques. One challenge with this flow is that once the hotspots are identified, a user still has to come up with OPC solutions to address the hotspots. This process can be cumbersome and time consuming as different types of hotspots with new designs may require different recipes, causing delays to tapeout. What is required is a robust, powerful and automated OPC technique that can handle various types of hotspots, so an automatic hotspot correction flow can be established. In this work, we introduce a new cost-function-based OPC technique called Co-optimization OPC that can be used to correct various types of hotspots with minimum tuning effort. In this approach, the OPC solver simultaneously solves for all the segments in a patch including main and sub-resolution assist features (SRAF), applying additional user-defined cost function constraints such as MEEF, PV band, MRC and SRAF printability. Unlike conventional OPC solvers, Cooptimization solvers can also move and grow SRAFs, which further improves the process window. The key benefit of the Co-optimization OPC solution is that it can be used in a standard recipe to resolve many different hotspots encountered across various designs for a given layer. In this study, we demonstrate that Co-optimization OPC can be successfully used to address various types of hotspots across designs for selected 2× nm node line/space layers, as an example. These layers have been particularly challenging as they use single-exposure lithography with k1 around 0.3. Aggressive RET solutions are required to address the patterning challenges for this layer. Finally, we will report on implementation of the Co-Optimization OPC Recipe within the FMO framework for hotspot correction.
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