Enhancing cell delay accuracy in post-placed netlists using ensemble tree-based algorithms

Nowadays, the ASIC design is increasing in complexity, and PPA targets are pushed to the limit. The lack of physical information at the early design stages hinders precise timing predictions and may lead to design re -spins. In previous work, we successfully improved timing prediction at the post -placement stage using the Random Forest model, achieving 91.25% cell delay accuracy. Building upon this, we further investigate the potential of Ensemble Tree-based algorithms, specifically focusing on " Extremely Randomized Trees "and " Gradient Boosting ", to close the gap in cell delay accuracy. In this paper, we enrich the training dataset with new 16 nm industrial designs. The results demonstrate a substantial improvement, with an average cell delay accuracy of 92.01% and 84.26% on unseen data. The average Root-Mean-Square-Error is significantly reduced from 12.11 to 3.23 and 7.76 on unseen data.