Data-driven fast electrostatics and TDDB aging analysis

Abstract

Computing the electric potential and electric field is a critical step for modeling and analysis of VLSI chips such as TDDB (Time dependent dielectric breakdown) aging analysis. Data-driven deep learning approach provides new perspectives for learning the physics-law and representations of the physics dynamics from the data. In this work, we propose a new data-driven learning based approach for fast 2D analysis of electric potential and electric fields based on DNNs (deep neural networks). Our work is based on the observation that the synthesized VLSI layout with multi interconnect layers can be viewed as layered images. Image transformation techniques via CNN (convolutional neural network) are adopted for the analysis. Once trained, the model is applicable to any synthesized layout of the same technology. Training and testing are done on a dataset built from a synthesized CPU chip. Results show that the proposed method is around 138x faster than the conventional numerical methods based software COMSOL, while keeping 99% of the accuracy on potential analysis, and 97% for TDDB aging analysis.