Francisco V. Fernández – författare

Aiming at the core of the problem, Reuse Based Methodologies and Tools in the Design of Analog and Mixed-Signal Integrated Circuits presents a framework for the reuse-based design of AMS circuits. The framework is founded on three key elements: (1) a CAD-supported hierarchical design flow that facilitates the incorporation of AMS reusable blocks, reduces the overall design time, and expedites the management of increasing AMS design complexity; (2) a complete, clear definition of the AMS reusable block, structured into three separate facets or views: the behavioral, structural, and layout facets, the first two for top-down electrical synthesis and bottom-up verification, the latter used during bottom-up physical synthesis; (3) the design for reusability set of tools, methods, and guidelines that, relying on intensive parameterization as well as on design knowledge capture and encapsulation, allows to produce fully reusable AMS blocks.

Reuse Based Methodologies and Tools in the Design of Analog and Mixed-Signal Integrated Circuits features a very detailed, tutorial, and in-depth coverage of all issues and must-have properties of reusable AMS blocks, as well as a thorough description of the methods and tools necessary to implement them. For the first time, this has been done hierarchically, covering one by one the different stages of the design flow, allowing us to examine how the reusable block yields its benefits, both in design time and correct performance.

This book describes a new design methodology that allows optimization-based synthesis of RF systems in a hierarchical multilevel approach, in which the system is designed in a bottom-up fashion, from the device level up to the (sub)system level.  At each level of the design hierarchy, the authors discuss methods that increase the design robustness and increase the accuracy and efficiency of the simulations. The methodology described enables circuit sizing and layout in a complete and automated integrated manner, achieving optimized designs in significantly less time than with traditional approaches.

Computational intelligence techniques are becoming more and more important for automated problem solving nowadays. Due to the growing complexity of industrial applications and the increasingly tight time-to-market requirements, the time available for thorough problem analysis and development of tailored solution methods is decreasing. There is no doubt that this trend will continue in the foreseeable future. Hence, it is not surprising that robust and general automated problem solving methods with satisfactory performance are needed.