Efficient Analog Integrated Circuit Sizing with GenAI: Exploring Generative Diffusion Models [Paperback]

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.

1.- Introduction.- 2. State-of-the-art.- 3. Proposed Architectures.- 4. Model Implementation and Optimization.- 5. Experimental Results.- 6. Conclusion and Future Works.

Pedro Eid received a B.Sc degree in Electrical and Computer Engineering from the Instituto Superior Técnico (IST), University of Lisbon, Portugal, in 2023. He is currently completing his M.Sc. degree in the same field. His research interests include Machine Learning and Deep Learning.

Filipe Azevedo received his M.Sc degree in Computer Science and Engineering from the Instituto Superior Técnico (IST), University of Lisbon, Portugal, in 2020. He is currently working on his PhD degree in Electrical and Computer Engineering from the same university, while working with Instituto de Telecomunicações. His research interests include Machine Learning and Generative AI applied to Analog IC Design Automation.

Nuno Lourenço received Licenciado, M.Sc., and Ph.D. degrees in Electrical and Computer Engineering from Instituto Superior Técnico, University of Lisbon, Portugal 2005, 2007, and 2014. He was also an invited Assistant Professor il*