Vlsi Design Methodology Development

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design.

A critical step in the design of a DSP system is to identify for each of its components (DSP kernels) an implementation architecture that provides the desired degree of flexibility/programmability and optimises the area-delay-power parameters. The book covers the entire solution space comprising both hardware multiplier-based and multiplex-less architectures that offer varying degrees of programmability.

For both the beginner and experienced Engineer using Vivado on the Zybo Z7 Xilinx Zynq FPGA Development Board.

The development of large-scale integrated systems on a chip has had a dramatic effect on circuit design methodology.
Recent years have seen an escalation of interest in systems level integration (system-on-a-chip) and the development of low power, high chip density circuits and systems. Kurt Hoffmann sets out to address a wide range of issues relating to the design and integration of integrated circuit components and provides readers with the methodology by which simple equations for the estimation of transistor geometries and circuit behaviour can be deduced. The broad coverage of this unique book ranges from field effect transistor design, MOS transistor modelling and the fundamentals of digital CMOS circuit design through to MOS memory architecture and design.