![]() ![]() Ricardo Martins received the B.Sc., M.Sc. His research interests include analog and mixed-signal IC design, electronic design automation tools, applied computational intelligence, and deep learning. He has authored or co-authored over 50 publications, including patents, books, book chapters, international journals and conferences papers. He is also an invited Assistant Professor in the Department of Electrical and Computer Engineering of IST-UL since 2015. He is with Instituto de Telecomunicações in Lisbon since 2005, where he now holds a postdoctoral research position. degrees in Electrical and Computer Engineering from Instituto Superior Técnico, University of Lisbon, Portugal, in 2005, 2007, and 2014 respectively. Nuno Lourenço (M14) received Licenciado, M.Sc. The proposed methodology was implemented and tested on two analog circuit topologies. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. This model can size a circuit given its specifications for a single topology. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuits performances as input features and devices sizes as target outputs. ![]() Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices sizes to circuits performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices sizes. It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. ![]() This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). ![]()
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