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Efficient Analog Integrated Circuit Sizing with GenAI:Exploring Generative Diffusion Models (SpringerBriefs in Applied Sciences and Technology)
by: Pedro H. M. Eid (Author), Filipe P. Azevedo (Author), Nuno C. C. Lourenço (Author), Ricardo M. F. Martins (Author)
Publisher: Springer
Publication Date: 2025-04-02
Language: English
Print Length: 95 pages
ISBN-10: 3031871049
ISBN-13: 9783031871047
Book Description
This book focuses on the automation of analog integrated circuit design, particularly the sizing process. It introduces an innovative approach leveraging generative artificial intelligence, specifically denoising diffusion probabilistic models (DDPM). The proposed methodology provides a robust solution for generating circuit designs that meet specific performance constraints, offering a significant improvement over conventional techniques. By integrating advanced machine learning models into the design workflow, the book showcases a transformative way to streamline the process while maintaining accuracy and reliability.
Editorial Reviews
This book focuses on the automation of analog integrated circuit design, particularly the sizing process. It introduces an innovative approach leveraging generative artificial intelligence, specifically denoising diffusion probabilistic models (DDPM). The proposed methodology provides a robust solution for generating circuit designs that meet specific performance constraints, offering a significant improvement over conventional techniques. By integrating advanced machine learning models into the design workflow, the book showcases a transformative way to streamline the process while maintaining accuracy and reliability.
