A 3-D Convolutional Network for Fast and Accurate S/Z-Parameters Prediction of Si-Interposer Power Distribution Network With Through Silicon Vias

In this brief, based on a 3D coarse model extraction method and the 3D convolutional neural network (3D CNN), a full-3D Artificial Intelligence(AI) method, namely C3D-CNN (Coarse 3D Convolutional Neural Network) is proposed to estimate the S-parameters and Z-parameters of interposer PDNs efficiently and accurately with TSVs considered. Three interposer PDN models with different metal densities and pitches are built to train and verify our method. Results of our model are compared with those obtained by a conventional finite element method (FEM) simulator (HFSS), a regular AI method (Fully Connected Neural Network, FCNN) and a conventional Machine Learning (ML) method (Gradient Boosting Decision Tree, GBDT). The calculation time of S-parameters and -parameters of a 4-port PDN model for 300 frequency points reduces from 600 seconds (HFSS) to 0.1 seconds by using our C3D-CNN method. Also, the accuracy of the proposed C3D-CNN method is 53.51% and 11.68% higher than that of FCNN and GBDT, respectively. The results show that our AI method can significantly reduce the calculation time and improve the accuracy of PDN analysis.