The summer school on VLSf GAD Tools and Applications was held from July 21 through August 1, 1986 at Beatenberg in the beautiful Bernese Oberland in Switzerland. The meeting was given under the auspices of IFIP WG 10. 6 VLSI, and it was sponsored by the Swiss Federal Institute of Technology Zurich, Switzerland. Eighty-one professionals were invited to participate in the summer school, including 18 lecturers. The 81 participants came from the following countries: Australia (1), Denmark (1), Federal Republic of Germany (12), France (3), Italy (4), Norway (1), South Korea (1), Sweden (5), United Kingdom (1), United States of America (13), and Switzerland (39). Our goal in the planning for the summer school was to introduce the audience into the realities of CAD tools and their applications to VLSI design. This book contains articles by all 18 invited speakers that lectured at the summer school. The reader should realize that it was not intended to publish a textbook. However, the chapters in this book are more or less self-contained treatments of the particular subjects. Chapters 1 and 2 give a broad introduction to VLSI Design. Simulation tools and their algorithmic foundations are treated in Chapters 3 to 5 and 17. Chapters 6 to 9 provide an excellent treatment of modern layout tools. The use of CAD tools and trends in the design of 32-bit microprocessors are the topics of Chapters 10 through 16. Important aspects in VLSI testing and testing strategies are given in Chapters 18 and 19.

In VLSI CAD, difficult optimization problems have to be solved on a constant basis. Various optimization techniques have been proposed in the past. While some of these methods have been shown to work well in applications and have become somewhat established over the years, other techniques have been ignored. Recently, there has been a growing interest in optimization algorithms based on principles observed in nature, termed Evolutionary Algorithms (EAs). Evolutionary Algorithms in VLSI CAD presents the basic concepts of EAs, and considers the application of EAs in VLSI CAD. It is the first book to show how EAs could be used to improve IC design tools and processes. Several successful applications from different areas of circuit design, like logic synthesis, mapping and testing, are described in detail. Evolutionary Algorithms in VLSI CAD consists of two parts. The first part discusses basic principles of EAs and provides some easy-to-understand examples. Furthermore, a theoretical model for multi-objective optimization is presented. In the second part a software implementation of EAs is supplied together with detailed descriptions of several EA applications. These applications cover a wide range of VLSI CAD, and different methods for using EAs are described. Evolutionary Algorithms in VLSI CAD is intended for CAD developers and researchers as well as those working in evolutionary algorithms and techniques supporting modern design tools and processes.

Symbolic Boolean manipulation using binary decision diagrams (BDDs) has been successfully applied to a wide variety of tasks, particularly in very large scale integration (VLSI) computer-aided design (CAD). The concept of decision graphs as an abstract representation of Boolean functions dates back to the early work by Lee and Akers. In the last ten years, BDDs have found widespread use as a concrete data structure for symbolic Boolean manipulation. With BDDs, functions can be constructed, manipulated, and compared by simple and efficient graph algorithms. Since Boolean functions can represent not just digital circuit functions, but also such mathematical domains as sets and relations, a wide variety of CAD problems can be solved using BDDs. `Binary Decision Diagrams and Applications for VLSI CAD provides valuable information for both those who are new to BDDs as well as to long time aficionados.' -from the Foreword by Randal E. Bryant. `Over the past ten years ... BDDs have attracted the attention of many researchers because of their suitability for representing Boolean functions. They are now widely used in many practical VLSI CAD systems. ... this book can serve as an introduction to BDD techniques and ... it presents several new ideas on BDDs and their applications. ... many computer scientists and engineers will be interested in this book since Boolean function manipulation is a fundamental technique not only in digital system design but also in exploring various problems in computer science.' - from the Preface by Shin-ichi Minato.

This book, and the research it describes, resulted from a simple observation we made sometime in 1986. Put simply, we noticed that many VLSI design tools looked "alike". That is, at least at the overall software architecture level, the algorithms and data structures required to solve problem X looked much like those required to solve problem X'. Unfortunately, this resemblance is often of little help in actually writing the software for problem X' given the software for problem X. In the VLSI CAD world, technology changes rapidly enough that design software must continually strive to keep up. And of course, VLSI design software, and engineering design software in general, is often exquisitely sensitive to some aspects of the domain (technology) in which it operates. Modest changes in functionality have an unfortunate tendency to require substantial (and time-consuming) internal software modifications. Now, observing that large engineering software systems are technology dependent is not particularly clever. However, we believe that our approach to xiv Preface dealing with this problem took an interesting new direction. We chose to investigate the extent to which automatic programming ideas cold be used to synthesize such software systems from high-level specifications. This book is one of the results of that effort.

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

Design Automation: Automated Full-Custom VLSI Layout Using the ULYSSES Design Environment deals with the use of the Ulysses design environment for an automated full-custom VLSI layout. Topics covered include VLSI chip design and design process, control mechanisms in Ulysses, and the use of artificial intelligence (AI) in design environments. An example design task is also presented. This book is comprised of 10 chapters and begins with an overview of VLSI computer-aided design (CAD), focusing on an expert system based design environment aimed at solving the CAD tool integration problem. An example CAD tool suite for such an environment is presented. The next chapter describes prior attempts at developing an integrated design environment, followed by a discussion on the computer-aided VLSI design process that motivated the development of the Ulysses design environment. The following chapters explore the use of AI techniques within Ulysses; the fundamental architecture of Ulysses; and the control mechanisms that govern the decision to execute various CAD tools, on particular files, within Ulysses. The implementation of Ulysses is also discussed. The final chapter demonstrates the feasibility of a knowledge-based design environment for VLSI chip design applications; the success of Ulysses at further automating the VLSI design process; and the usability of Ulysses as a VLSI design environment. This monograph will be a valuable resource for systems designers and other practitioners in computer science and computer engineering.