| Summary: | The book has 13 chapters and is divided into 2 parts. The first part deals with: Response surface modeling; Machine learning; Data-driven and physics-based modeling; and Verification of modeling: metrics and methodologies. The second part covers: An overview of modern, automated analog circuit modeling methods: similarities, strengths, and limitations; On the usage of machine-learning techniques for the accurate modeling of integrated inductors for RF applications; Modeling of variability and reliability in analog circuits; Modeling of pipeline ADC functionality and nonidealities; Power systems modelling; A case study for MEMS modelling: efficient design and layout of 3D accelerometer by automated synthesis; and Spintronic resistive memories: sensing schemes. Introduction and Conclusion are also given.
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