A pattern replacement in-design auto-fixing methodology, called MAS-POP, is developed to increase the scores calculated by the Manufacturability Analysis and Scoring (MAS) tool, improving the compliance with DFM rules. A library of patterns is developed using pattern classification automation, converting multiple types of Back-End-Of-Line (BEOL) DFM rules to patterns: via-metal line end enclosure, metal 2 tip-to-tip spacing, and metal area. Corresponding fixing hints are prescribed for each pattern. Once the library of patterns and the associated fixing hints have been developed, they are integrated with the router to utilize its pattern replacement feature. This insertion identifies matching patterns and fixes the violations by applying the prescribed fixing hints, improving the usage of the DFM rules and enhancing the MAS scores. The MAS-POP methodology is demonstrated on routed designs. Results show that for a 200 x 200 um2 block, three via-metal line end enclosure patterns reduce the number of DFM violations from 12.5k to 360 on one 2x metal layer, with a small runtime impact.
KEYWORDS: Data modeling, Machine learning, Neural networks, Design and modelling, Design for manufacturing, Principal component analysis, Correlation coefficients, Singular value decomposition, Mathematical optimization, Lithography
Design for Manufacturability (DFM) physical verification checks using supervised Machine Learning (ML) are developed and optimized to identify via-metal enclosure weak points to prevent via opens caused by line-end shortening post-retargeting. Various methods for generating feature vectors and neural network architectures are evaluated for optimizing training time and ML model quality. Techniques include applying PCA to image-based density vectors generated from layout clips to identify the principle components or using localized layout features directly for model training. Results show that for a sample size of 300k vias, the image-based density vectors versus localized layout feature vectors achieve similar correlation coefficients of 0.95 and normalized RMSE of 0.11, with a training time of 10+ hours versus 1+ minute, respectively.
Retargeting-aware Design for Manufacturability (DFM) via-metal enclosure checks are developed using supervised machine learning to identify critical weak points to aid layout fixing. The machine learning model is developed using a neutral network architecture. Seventeen localized layout features were extracted, including: side and line end via-metal enclosure, via spacing to the neighboring features, and metal coloring. The extracted features were used to form feature vectors to train and generate a machine learning-based model for predicting post-retargeting, via-metal enclosures. This method was demonstrated on 22nm layouts. Using a neural network with 2-hidden layers, the predicted via-metal enclosure versus the actual data correlate with an R2 of 0.91 and an RMSE 0.0067.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.