ML-Based Wire RC Prediction in Monolithic 3D ICs with an Application to Full-Chip Optimization

Abstract

The state-of-the-art Monolithic 3D (M3D) IC design methodologies~\citem3d:Ku-tcad-Compact2D, m3d:Panth-tcad-Shrunk2D use commercial electronic design automation tools built for 2D ICs to implement a pseudo-3D design and split it into two dies that are routed independently to create an M3D design. Therefore, an accurate estimation of 3D wire parasitics at the pseudo-3D stage is important to achieve a well optimized M3D design. In this paper, we present a regression model based on boosted decision tree learning to better predict the 3D wire parasitics (RCs) at the pseudo-3D stage. Our model is trained using individual net features as well as the full-chip design metrics using multiple instantiations of 8 different netlists and is tested on 3 unseen netlists. Compared to the Compact-2D~\citem3d:Ku-tcad-Compact2D flow on its own as the reference pseudo-3D, the addition of our predictive model achieves up to $2.9 \times$ and $1.7 \times$ smaller root mean square error in the resistance and capacitance predictions respectively. On an unseen netlist design, we observe that our model provides 98.6% and 94.6% RC prediction accuracy in 3D and up to $6.4 \times$ smaller total negative slack of the design compared to the result of Compact-2D flow resulting in a more timing-robust M3D IC. This model is not limited to Compact-2D, and can be extended to other pseudo-3D flows.