Advanced Performance Estimation of Analog Layouts Using Convolutional Neural Networks

Designing high-performance analog circuits involves navigating intricate interactions between layout details and circuit performance metrics. Traditional methods often rely on extensive post-layout simulations to accurately predict circuit behavior. However, these simulations are computationally intensive and may become impractical for optimizing large-scale designs. To address these challenges, this paper introduces a pioneering approach that leverages convolutional neural networks (CNNs) to predict analog circuit performance directly from layout data. By analyzing layout features and parasitic elements, our CNN architecture enhances prediction accuracy while reducing the need for exhaustive simulations. This method not only streamlines the design process but also offers scalability, with promisingly significant advancements in analog layout automation and circuit performance prediction.