Analog Integrated Circuit Design Under PVT Conditions: Efficient Reinforcement and Transfer Learning Techniques (SpringerBriefs in Applied Sciences and Technology)

This book delivers a focused, technical exploration of automated analog and RF integrated circuit sizing under process, voltage, and temperature variations, guiding readers through foundational concepts, current methodologies, and advanced machine‑learning‑driven approaches. It first examines multiple reinforcement‑learning‑based strategies for embedding PVT conditions directly into modern sizing flows, clarifying their conceptual differences and practical implications. It then explores a complementary deep‑learning‑assisted approach that leverages ANN‑based performance regressors, transfer learning, and adaptive refinement to accelerate simulation‑driven optimization without requiring extensive corner‑specific datasets. Together, these chapters provide a grounded overview of current techniques and ongoing developments in automated analog IC design.

Pedro Paiva 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 field of control, robotics, and artificial intelligence at the same institution, with his thesis focusing on electronic design automation of analog integrated circuits. His research interests include machine learning and deep learning.

José Costa received a B.Sc. degree in Electrical and Computer Engineering from the Instituto Superior Técnico (IST), University of Lisbon, Portugal, in 2025. 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 Ph.D. 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.

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 Assistant Professor of the electronics scientific area of the Department of Electrical and Computer Engineering of IST-UL.