In this paper, we present our innovative work of using Siemens EDA Calibre® Machine Learning (ML) assisted Optical and Process Correction (OPC) verification tool to effectively capture all kinds of hotspots using one single constraint across the whole layout for each failing mechanism, for example one constraint for bridging failing mechanism, one constraint for pinching failing mechanism, etc. The pattern differentiation is accomplished by ML classifier. The output data volume is controlled by using classification limiting function instead of tuned constraints. This work significantly improves the effectiveness of capturing and not missing real hotspots yet simplifies the OPC verification recipe setup and engineering workload. The unique hotspots count on full chip using this new strategy can be at thousand level. This makes the Machine Learning assisted hotspot capture new strategy practical to prepare hotspot monitoring points for wafer verification, for example SEM inspection.
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