No abstract

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 an d 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 3 00k 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.