Fast and Accurate Wire Timing Estimation on Tree and Non-Tree Net Structures

Timing optimization is repeatedly performed throughout the entire design flow. The long turn-around time of querying a sign-off timer has become a bottleneck. To break through the bottleneck, a fast and accurate timing estimator is desirable to expedite the pace of timing closure. Unlike gate timing, which is calculated by interpolating lookup tables in cell libraries, wire timing calculation has remained a mystery in timing analysis. The mysterious formula and complex net structures increase the difficulty to correlate with the results generated by a sign-off timer, thus further preventing incremental timing optimization engines from accurate timing estimation without querying a sign-off timer. We attempt to solve the mystery by a novel machine-learning-based wire timing model. Different from prior machine learning models, we first extract topological features to capture the characteristics of RC networks. Then, we propose a loop breaking algorithm to transform non-tree nets into tree structures, and thus non-tree nets can be handled in the same way as tree-structured nets. Experiments are conducted on four industrial designs with tree-like nets (28nm) and two industrial designs with non-tree nets (16nm). Our results show that the prediction model trained by XGBoost is highly accurate: For both tree-like and non-tree nets, the mean error of wire delay is lower than 2 ps. The predicted path arrival times have less than 1% mean error. Experimental results also demonstrate that our model can be trained only once and applied to different designs using the same manufacturing process. Our fast and accurate wire timing prediction can easily be integrated into incremental timing optimization and expedites timing closure.