Generative Adversarial Learning Based Power Noise Induced Eye Diagram Estimation Method for High-Speed Channel Design

In this article, we present a novel power noise induced eye diagram estimation method for high-speed channel design with generative adversarial learning. By leveraging a tailored adaptive Gramian-Angular-Field segmentation integration (AGSI) framework with generative adversarial networks (GANs), the proposed AGSI–GAN accurately and efficiently estimates eye diagrams under challenging design scenarios involving signal integrity (SI) and power integrity (PI) interactions, such as crosstalk and switching noise. Our approach, AGSI–GAN, enhances the U-Net generator’s learning efficiency by utilizing AGSI as condition images that encapsulate domain characteristics related to SI/PI. By integrating single-bit response, far-end crosstalk and simultaneous switching noise as modular components, AGSI converts this integrated data into images to enable both the generator and the discriminator interpret condition images with metafeatures. The trained AGSI–GAN reduces runtime by 88.6% compared to full-transient simulation, while maintaining high accuracy with all eye diagram metrics, including cumulative areas, showing average mean absolute percentage errors below 3% . Furthermore, AGSI–GAN facilitates accelerated design optimization while enabling the estimation of complete eye diagrams for the defined objective function, effectively addressing complex SI/PI tradeoffs. The framework shows significant potential for expediting the optimization process, integrating advanced frameworks, and adapting to complex and emerging channel designs.