Machine Learning for Parasitic Estimation in Advanced IC Design Flows

This article presents a machine learning-based framework for predicting parasitic effects in integrated circuit designs. Traditional parasitic extraction requires computationally intensive electromagnetic field solvers that become increasingly demanding at advanced technology nodes. The proposed approach leverages historical design data to train models that can estimate parasitic values early in the design process with reasonable accuracy and significantly reduced runtime. A comprehensive feature engineering pipeline captures geometric parameters, layer information, and topological characteristics from layout data. Various machine learning algorithms are evaluated, including gradient boosting, neural networks, and ensemble methods, with specific optimizations for resistance and capacitance prediction. The framework integrates into existing EDA tools through a plugin architecture that operates across multiple design stages, providing progressive refinement of estimates as designs evolve. Experimental validation across multiple technology nodes demonstrates substantial runtime improvements compared to traditional extraction while maintaining acceptable accuracy for early design phases. Case studies on real-world SoCs show improved design convergence through earlier parasitic awareness. The article identifies limitations and potential enhancements, including variation-aware modeling and applications to emerging interconnect technologies.