Springer Variation-Aware Analog Structural Synthesis: A Computational Intelligence Approach (Analog Circuits and Signal Processing)

This book describes computational intelligence-based tools for robust design of analog circuits. It starts with global variation-aware sizing and knowledge extraction and progressively extends to variation-aware topology design. Review From the reviews:“This book is squarely aimed at analog circuit designers who are searching for new approaches to analog structural design and optimization. … Those most likely to benefit from this book … are experts in the field of industrial circuit design seeking insight into new design tools. … for the non-expert, with only a cursory understanding of the background material, the processes and results described in this book are an inspiring example of real-world applications of evolutionary design.” (John Rieffel, Genetic Programming and Evolvable Machines, Vol. 12, 2011) From the Back Cover Variation-Aware Analog Structural Synthesis describes computational intelligence-based tools for robust design of analog circuits. It starts with global variation-aware sizing and knowledge extraction, and progressively extends to variation-aware topology design. The computational intelligence techniques developed in this book generalize beyond analog CAD, to domains such as robotics, financial engineering, automotive design, and more. The tools are for: Globally-reliable variation-aware automated sizing via SANGRIA, leveraging structural homotopy and response surface modeling. Template-free symbolic models via CAFFEINE canonical form functions, for greater insight into the relationship between design/process variables and circuit performance/robustness. Topology selection and topology synthesis via MOJITO. 30 well-known analog building blocks are hierarchically combined, leading to >100,000 different possible topologies which are all trustworthy by construction. MOJITO does multi-objective genetic programming-based search across these topologies with SPICE accuracy, to return a set of sized topologies on the optimal performance/yield tradeoff curve. Nonlinear sensitivity analysis, topology decision trees, and analytical tradeoffs. With a data-mining perspective on Pareto-optimal topologies, this book shows how to do global nonlinear sensitivity analysis on topology and sizing variables, automatically extract a specs-to-topology decision tree, and determine analytical expressions of performance tradeoffs. Novel topology design. The MOJITO-N and ISCLEs tools generate novel yet trustworthy topologies; including boosting digitally-sized circuits for analog functionality. About the Author Trent McConaghy is co-founder and Chief Scientific Officer of Solido Design Automation Inc. He was a co-founder and Chief Scientist of Analog Design Automation Inc., which was acquired by Synopsys Inc. in 2004. Prior to that, he did research for the Canadian Department of National Defense. He received his PhD degree in Electrical Engineering from the Katholieke Universiteit Leuven, Belgium, in 2008. He received a Bachelor’s in Engineering (with great distinction), and a Bachelor’s in Computer Science (with great distinction), both from the University of Saskatchewan, Canada, in 1999. He has about 40 peer-reviewed technical papers and patents granted / pending. He has given invited talks / tutorials at many labs, universities, and conferences such as JPL, MIT, ICCAD, and DAC. He is regularly a technical program committee member and reviewer in both the CAD and intelligent systems fields, such as IEEE Trans CAD, ACM TODAES, Electronics Letters, to IEEE Trans Evolutionary Computation, the Journal of Genetic Programming and Evolvable Machines, GPTP, GECCO, ICES, etc. His research interest is in statistical machine learning and intelligent systems, with transistor-level CAD applications such as variation-aware design, analog topology design, automated sizing, knowledge extraction, and symbolic modeling. Michiel Steyaert was born in Aalst, Belgium, i