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.
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 in the Department of Electrical and Computer Engineering of IST-UL from 2015 to 2019, where he was distinguished with two “IST Outstanding Teaching Awards.” He has been with Instituto de Telecomunicações in Lisbon since 2005, where he is a researcher and an invited Assistant Professor in the Department of Informatics at the University of Évora since 2021. He has authored, co-authored, and supervised over 80 international scientific publications, including two patents, seven books, three book chapters, 23 international journals, and 47 conference papers, and is/was a supervisor in 2 Ph.D. thesis and 8 M.Sc. dissertations. He is/was involved in the Organizing Committee of several International Conferences such as IEEE ISCAS’15, PRIME’16-21, or SMACD’16-21. He is/was the Publication Co-Chair of the International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design 2016, 2017, 2019, and 2021, and of the Conference on Ph.D. Research in Microelectronics and Electronics (PRIME) 2016, 2019, and 2021 was technically sponsored by IEEE, IEEE CEDA, and IEEE CAS societies. He is the General Chair of SMACD 2022. He has received 12 Scientific Awards and Distinctions, including several “best paper awards,” the “Best EDA tool” from SMACD’15 competition, and the “2010 IET DesignVision Award in the category of Semiconductor IP.” He has participated in several Scientific projects with national and international Universities and Companies, and he is the Principal Investigator of the ongoing internal HAICAS project funded by IT. His current research interests include AMS/RF IC design, Evolutionary Computation, and Machine Learning applied to Electronic Design Automation and Applied Artificial Intelligence.
Ricardo Martins received the Ph.D. degree in Electrical and Computer Engineering from Instituto Superior Técnico – University of Lisbon (IST-UL), Portugal, in 2015. He is with Instituto de Telecomunicações since 2011 developing electronic design automation tools, and in 2022 became an Assistant Professor of the Electronics scientific area of the Department of Electrical and Computer Engineering of IST-UL. He has participated in more than 100 scientific publications, including first authorships and supervisions in several books and high impact-factor journals. His research interests are: electronic design automation tools for analog, mixed-signal, radio-frequency and millimeter wave integrated circuits; deep nanometer integration technologies; and, applied soft computing, machine and deep learning.
