دانلود کتاب EDA برای پیاده‌سازی IC، طراحی مدار و فناوری فرآیند

When Taylor & Francis Group (CRC Press) first suggested an update to the 2015 second edition of the EDA Handbook, we realized that another decade had passed in the electronics industry, almost a complete era in the geological sense. We agreed that the changes in EDA and design methods warranted another once-in-a-decade update and asked our second edition group of authors to update their chapters. Our original division of topics into “front end” and “back end” still seemed to work well. In addition, the list of chapters from the second edition worked for new topics such as AI/ML chiplets and RISC-V, and so, these topics did not require new chapters. Finding all the second edition authors was a challenge. Some had retired or moved out of the industry, some had moved to completely new roles, and some were just too busy to contemplate a revision. Some, very unfortunately, had passed away—not unexpected in the 20 years since the first edition, but very regrettable. However, many were still available and happy to revise their chapters. Where appropriate, we recruited new coauthors to update most chapters, highlighting the major changes that occurred during the last decade. We have updated the contributor lists to reflect the current status, as far as we could determine. LinkedIn has been a big help, even if it needs double-checking. It seems appropriate to quote from our original first edition preface: “As we look at the state of electronics and IC design in 2005–2006, we see that we may soon enter a major period of change in the discipline.” And “Upon further consideration, it is clear that the current EDA approaches have a lot of life left in them.” This has been our continued finding in doing this revision. Some rather revolutionary changes are still coming, but to a great extent, most of the EDA algorithms, tools, and methods have gone through evolutionary, rather than revolutionary, change over the last decade. Most of the major updates have occurred both in the initial phases of the design flow, where the level of abstraction keeps rising in order to support more functionality with lower NRE costs, and in the final phases, where the complexity due to smaller and smaller geometries is compounded by the slow progress of shorter wavelength lithography. Machine learning has been affecting this field, like everything else in our lives, by capitalizing on decades of design data and experience and helping designers bring larger, more performant, and more complex circuits to market ever faster. Major challenges faced by the EDA industry and researchers do not so much require revising previously accumulated knowledge, but rather stimulate applying it in new ways and, in some cases, developing new approaches. We hope that the readers enjoy the improved depth and the broader topic coverage offered by the third edition.