1 research outputs found
Context-Aware DFM Rule Analysis and Scoring Using Machine Learning
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