NFGen: Normalizing Flow-Based Joint Generative Model for Variability-Aware Design Technology Co-Optimization

Abstract

As transistor sizes continue shrinking, the impacts of variability have become ever more paramount in circuit design and manufacturing. Their accurate representations in model cards help save design margins and provide appropriate guidelines in design technology co-optimization (DTCO). To address such a challenge, we propose a novel machine learning framework, normalizing flow-based joint generative (NFGen) model, which generates a comprehensive model library from a limited number of model cards. Unlike traditional generative methods that focus on the marginal distribution of model card parameters, NFGen is the first model to approximate their joint distribution, which includes information on their correlation and thus enables closer representation of variability effects. In addition, we introduce two similarity metrics to rigorously evaluate the quality of generated model cards. Experimental results show that NFGen reduces overall error by $2\times $ to $8\times $ compared to state-of-the-art methods, validating its superiority in variability-aware DTCO.