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.
Design for Manufacturability (DFM) in-design fixing methodologies are developed to improve Manufacturability Aware Scoring (MAS). Two methodologies have been evaluated. For the first methodology, DFM recommended rules are inserted in the reference flow for rip-up-and-reroute, thus fixing DFM rule violations, improving the MAS score. For the second methodology, pattern classification is used to classify the recommended rules into patterns based on the profiling of multiple layout designs. A library of fixable patterns with corresponding fixes is built. The pattern library is then inserted in the rip-up-and-reroute flow to fix the DFM rule violations, improving the MAS score. The methodologies are demonstrated on 28nm technology. Results show an average fix rate of 89.1 % for a design with a core utilization of 0.6 and 78.4% with a core utilization of 0.6 for three DFM MAS enclosure rules, VIA2, VIA3 and VIA4 layers.
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.
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