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- W2029229890 abstract "Early lithographic hotspot detection has become increasingly important in achieving lithography-friendly designs andmanufacturability closure. Fast physical verification tools employing pattern matching or machine learning techniqueshave emerged as great options for detecting hotspots in the early design stages. In this work, we propose acharacterization methodology that provides measurable quantification of a given hotspot detection tool's capability tocapture a previously seen or unseen hotspot pattern. Using this methodology, we conduct a side-by-side comparison oftwo hotspot detection methods-one using pattern matching and the other based on machine learning. The experimentalresults reveal that machine learning classifiers are capable of predicting unseen samples but may mispredict some of itstraining samples. On the other hand, pattern matching-based tools exhibit poorer predictive capability but guarantee fulland fast detection on all their training samples. Based on these observations, we propose a hybrid detection solution thatutilizes both pattern matching and machine learning techniques. Experimental results show that the hybrid solutioncombines the strengths of both algorithms and delivers improved detection accuracy while sacrificing little runtimeefficiency." @default.
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- W2029229890 date "2011-03-17" @default.
- W2029229890 modified "2023-09-23" @default.
- W2029229890 title "Efficient approach to early detection of lithographic hotspots using machine learning systems and pattern matching" @default.
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- W2029229890 doi "https://doi.org/10.1117/12.879546" @default.
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