Fast and Accurate Library Generation Leveraging Deep Learning for OCV Modelling

摘要

Statistical timing characterization for modeling On-Chip Variation (OCV) is critical in current technology nodes to avoid over-design and to improve design convergence and predictability. OCV characterization, however, is resource intensive as it involves running millions of Monte-Carlo spice simulations to cover different timing arcs for multiple cells in standard-cell library. We have developed a neural network model that fully comprehends multiple cell types to model cell propagation delays as well as OCV sigma at target process-voltage-temperature (PVT) corners with a significantly reduced number of simulations. The proposed method generates Liberty Variation Format (LVF) models which are the latest and most accurate representation of OCV margin in the industry’s standard tools and flows.On extensive testing with 7 million OCV delay values in 10nm node, we attained 60% reduction in runtime while maintaining prediction-error less than 5% for 99.98% arcs which can be used for early timing integration.