A Deep-Learning-Based Statistical Timing Prediction Method for Sub-16nm Technologies

Pre-routing timing estimation is vital but challenging since accurate net information is available only after routing and parasitic extraction. Existing methodologies predict the timing metrics with the help of the placement information of standard cells. However, neglecting the analysis of process variation effects hinders the precision of those methodologies, especially in sub-16nm technologies as delay distributions become asymmetric. Therefore, a deep-learning-based statistical timing prediction method is proposed to model process variation effects in the pre-routing stage. Congestion features and pin-to-pin features are fed into graph neural networks for post-routing interconnect parasitic and arc delay prediction. Moreover, a calibration method is proposed to compensate for the precision loss of the delay propagation. We evaluate our methods using open-source designs and EDA tools, which demonstrate improved accuracy in pre-routing timing prediction methods and a remarkable speed-up compared to traditional routing and timing analysis process.