Generative Model Based Standard Cell Timing Library Characterization

Accurate cell timing characterization is essential, on which static timing analysis relies to verify timing performance and ensure design robustness across various PVT conditions (corners). The corner explosion in modern design amplifies the efficiency and scalability challenge for accurate characterization. However, the conventional characterization approach of SPICE simulation alone becomes prohibitively expensive due to the increasing computational complexity and the amount of characterized data. In this paper, we view the characterization problem from a generative modeling perspective to tackle the efficiency and scalability challenge. With a hybrid of generative adversarial network (GAN) and autoencoder, our generative model learns and generalizes among various timing arcs and corners. Experimental results demonstrate that the proposed framework achieves high accuracy and extensibility while reducing the runtime significantly.