2018/08/16 by Vikas Tripathi, Valerio Pérez, Valerio Perez +11
Computer Science · Engineering · #Advancements in Photolithography Techniques #Artificial intelligence #Computer science #Context (archaeology) #Data mining #Design for manufacturability #Engineering #FOS: Computer and information sciences #Injection Molding Process and Properties #Lithography #Machine learning #Manufacturing Process and Optimization #Mechanical engineering #Other Computer Science (cs.OH) #Quality (philosophy) #Reliability engineering #cs.OH
paper · pdf · doi:10.48550/arxiv.1808.05999
arxiv created 2018/08/16 · openalex publication_date 2018/08/16 · arxiv updated 2018/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To evaluate the quality of physical layout designs in terms of manufacturability, DFM rule scoring techniques have been widely used in physical design and physical verification phases. However, one major drawback of conventional DFM rule scoring methodologies is that resultant DFM rule scores may not accurate since the scores may not highly correspond to lithography simulation results. For instance, conventional DFM rule scoring methodologies usually use rule-based techniques to compute scores without considering neighboring geometric scenarios of targeted layout shapes. That can lead to inaccurate scoring results since computed DFM rule scores can be either too optimistic or too pessimistic. Therefore, in this paper, we propose a novel approach with the use of machine learning technology to analyze the context of targeted layouts and predict their lithography impacts on manufacturability.