Learning From Vlsi Design Experience

This book shares with readers practical design knowledge gained from the author’s 24 years of IC design experience. The author addresses issues and challenges faced commonly by IC designers, along with solutions and workarounds. Guidelines are described for tackling issues such as clock domain crossing, using lockup latch to cross clock domains during scan shift, implementation of scan chains across power domain, optimization methods to improve timing, how standard cell libraries can aid in synthesis optimization, BKM (best known method) for RTL coding, test compression, memory BIST, usage of signed Verilog for design requiring +ve and -ve calculations, state machine, code coverage and much more. Numerous figures and examples are provided to aid the reader in understanding the issues and their workarounds.

This book shares with readers practical design knowledge gained from the author’s 24 years of IC design experience. The author addresses issues and challenges faced commonly by IC designers, along with solutions and workarounds. Guidelines are described for tackling issues such as clock domain crossing, using lockup latch to cross clock domains during scan shift, implementation of scan chains across power domain, optimization methods to improve timing, how standard cell libraries can aid in synthesis optimization, BKM (best known method) for RTL coding, test compression, memory BIST, usage of signed Verilog for design requiring +ve and -ve calculations, state machine, code coverage and much more. Numerous figures and examples are provided to aid the reader in understanding the issues and their workarounds.

The complexity of modern chip design requires extensive use of specialized software throughout the process. To achieve the best results, a user of this software needs a high-level understanding of the underlying mathematical models and algorithms. In addition, a developer of such software must have a keen understanding of relevant computer science aspects, including algorithmic performance bottlenecks and how various algorithms operate and interact. This book introduces and compares the fundamental algorithms that are used during the IC physical design phase, wherein a geometric chip layout is produced starting from an abstract circuit design. This updated second edition includes recent advancements in the state-of-the-art of physical design, and builds upon foundational coverage of essential and fundamental techniques. Numerous examples and tasks with solutions increase the clarity of presentation and facilitate deeper understanding. A comprehensive set of slides is available on the Internet for each chapter, simplifying use of the book in instructional settings. “This improved, second edition of the book will continue to serve the EDA and design community well. It is a foundational text and reference for the next generation of professionals who will be called on to continue the advancement of our chip design tools and design the most advanced micro-electronics.” Dr. Leon Stok, Vice President, Electronic Design Automation, IBM Systems Group “This is the book I wish I had when I taught EDA in the past, and the one I’m using from now on.” Dr. Louis K. Scheffer, Howard Hughes Medical Institute “I would happily use this book when teaching Physical Design. I know of no other work that’s as comprehensive and up-to-date, with algorithmic focus and clear pseudocode for the key algorithms. The book is beautifully designed!” Prof. John P. Hayes, University of Michigan “The entire field of electronic design automation owes the authors a great debt for providing a single coherent source on physical design that is clear and tutorial in nature, while providing details on key state-of-the-art topics such as timing closure.” Prof. Kurt Keutzer, University of California, Berkeley “An excellent balance of the basics and more advanced concepts, presented by top experts in the field.” Prof. Sachin Sapatnekar, University of Minnesota

This book is a comprehensive guide to new DFT methods that will show the readers how to design a testable and quality product, drive down test cost, improve product quality and yield, and speed up time-to-market and time-to-volume. Most up-to-date coverage of design for testability. Coverage of industry practices commonly found in commercial DFT tools but not discussed in other books. Numerous, practical examples in each chapter illustrating basic VLSI test principles and DFT architectures.

Based on the highly successful second edition, this extended edition of SystemVerilog for Verification: A Guide to Learning the Testbench Language Features teaches all verification features of the SystemVerilog language, providing hundreds of examples to clearly explain the concepts and basic fundamentals. It contains materials for both the full-time verification engineer and the student learning this valuable skill. In the third edition, authors Chris Spear and Greg Tumbush start with how to verify a design, and then use that context to demonstrate the language features, including the advantages and disadvantages of different styles, allowing readers to choose between alternatives. This textbook contains end-of-chapter exercises designed to enhance students’ understanding of the material. Other features of this revision include: New sections on static variables, print specifiers, and DPI from the 2009 IEEE language standard Descriptions of UVM features such as factories, the test registry, and the configuration database Expanded code samples and explanations Numerous samples that have been tested on the major SystemVerilog simulators SystemVerilog for Verification: A Guide to Learning the Testbench Language Features, Third Edition is suitable for use in a one-semester SystemVerilog course on SystemVerilog at the undergraduate or graduate level. Many of the improvements to this new edition were compiled through feedback provided from hundreds of readers.

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. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

The Complete, Modern Tutorial on Practical VLSI Chip Design, Validation, and Analysis As microelectronics engineers design complex chips using existing circuit libraries, they must ensure correct logical, physical, and electrical properties, and prepare for reliable foundry fabrication. VLSI Design Methodology Development focuses on the design and analysis steps needed to perform these tasks and successfully complete a modern chip design. Microprocessor design authority Tom Dillinger carefully introduces core concepts, and then guides engineers through modeling, functional design validation, design implementation, electrical analysis, and release to manufacturing. Writing from the engineer’s perspective, he covers underlying EDA tool algorithms, flows, criteria for assessing project status, and key tradeoffs and interdependencies. This fresh and accessible tutorial will be valuable to all VLSI system designers, senior undergraduate or graduate students of microelectronics design, and companies offering internal courses for engineers at all levels. Reflect complexity, cost, resources, and schedules in planning a chip design project Perform hierarchical design decomposition, floorplanning, and physical integration, addressing DFT, DFM, and DFY requirements Model functionality and behavior, validate designs, and verify formal equivalency Apply EDA tools for logic synthesis, placement, and routing Analyze timing, noise, power, and electrical issues Prepare for manufacturing release and bring-up, from mastering ECOs to qualification This guide is for all VLSI system designers, senior undergraduate or graduate students of microelectronics design, and companies offering internal courses for engineers at all levels. It is applicable to engineering teams undertaking new projects and migrating existing designs to new technologies.

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.