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Titlebook: Variation-Aware Analog Structural Synthesis; A Computational Inte Trent McConaghy,Pieter Palmers,Georges Gielen Book 2009 Springer Science+

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書目名稱Variation-Aware Analog Structural Synthesis
副標(biāo)題A Computational Inte
編輯Trent McConaghy,Pieter Palmers,Georges Gielen
視頻videohttp://file.papertrans.cn/981/980554/980554.mp4
概述A roadmap for analog CAD and its implementation algorithm-by-algorithm, presented by notable figures in analog CAD and analog design field who have experience in both the academia and industry.A compe
叢書名稱Analog Circuits and Signal Processing
圖書封面Titlebook: Variation-Aware Analog Structural Synthesis; A Computational Inte Trent McConaghy,Pieter Palmers,Georges Gielen Book 2009 Springer Science+
描述This book describes new tools for front end analog designers, starting with global variation-aware sizing, and extending to novel variation-aware topology design. The tools aid design through automation, but more importantly, they also aid designer insight through automation. We now describe four design tasks, each more general than the previous, and how this book contributes design aids and insight aids to each. The ?rst designer task targeted is global robust sizing. This task is supported by a design tool that does automated, globally reliable, variation-aware s- ing (SANGRIA),and an insight-aiding tool that extracts designer-interpretable whitebox models that relate sizings to circuit performance (CAFFEINE). SANGRIA searches on several levels of problem dif?culty simultaneously, from lower cheap-to-evaluate “exploration” layers to higher full-evaluation “exploitation” layers (structural homotopy). SANGRIAmakes maximal use of circuit simulations by performing scalable data mining on simulation results to choose new candidate designs. CAFFEINE accomplishes its task by tre- ing function induction as a tree-search problem. It constrains its tree search space via a canonical-functio
出版日期Book 2009
關(guān)鍵詞Analysis; algorithms; artificial intelligence; computational intelligence; database; integrated circuit; k
版次1
doihttps://doi.org/10.1007/978-90-481-2906-5
isbn_softcover978-94-007-2608-6
isbn_ebook978-90-481-2906-5Series ISSN 1872-082X Series E-ISSN 2197-1854
issn_series 1872-082X
copyrightSpringer Science+Business Media B.V. 2009
The information of publication is updating

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Variation-Aware Sizing: Background,07]. Yield optimization/design centering for the sizing task can play a useful role within a statistical design toolset, to speed a designer’s overall robust-design effort. Accordingly, there has been much research effort aimed at building analog circuit yield optimizers. This chapter examines the p
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Globally Reliable, Variation-Aware Sizing: Sangria,e in mind, to achieve the yield optimizer tool requirements of Chap. 2: accuracy, efficiency, scalability, flexibility, and global reliability..To be accurate and flexible, SANGRIA uses SPICE-in-the-loop, and an accurate model of manufacturing variation [Dre1999]. To be efficient in light of expensi
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Variation-Aware Topology Synthesis and Knowledge Extraction,ng). In Chaps. 6–7, MOJITO did topology synthesis (nominally). This chapter does both: variation-aware topology synthesis. By combining the two features at once, we bias the MOJITO search towards topologies that are robust, resulting in the MOJITO-R tool [Mcc2009d]. It also does knowledge extraction
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