Variation-aware analog structural synthesis
Variation-Aware Analog Structural Synthesis describes computational intelligence-based tools for robust design of analog circuits. It starts with global variation-aware sizing and knowledge extraction, and progressively extends to variation-aware topology design. The computational intelligence techn...
保存先:
| 主要な著者: | , , , , |
|---|---|
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
Dordrecht :
Springer Netherlands
[20..].
Cham : Springer Nature |
| シリーズ: | Analog Circuits and Signal Processing
|
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Variation-aware analog structural synthesis, a computational intelligence approach, Trent McConaghy ... [et al.], Dordrecht, Springer, 2009, 1 vol. (XX-305 p.), Analog circuits and signal processing series, 978-90-481-2905-8 |
| 要約: | Variation-Aware Analog Structural Synthesis describes computational intelligence-based tools for robust design of analog circuits. It starts with global variation-aware sizing and knowledge extraction, and progressively extends to variation-aware topology design. The computational intelligence techniques developed in this book generalize beyond analog CAD, to domains such as robotics, financial engineering, automotive design, and more. The tools are for: Globally-reliable variation-aware automated sizing via SANGRIA, leveraging structural homotopy and response surface modeling. Template-free symbolic models via CAFFEINE canonical form functions, for greater insight into the relationship between design/process variables and circuit performance/robustness. Topology selection and topology synthesis via MOJITO. 30 well-known analog building blocks are hierarchically combined, leading to >100,000 different possible topologies which are all trustworthy by construction. MOJITO does multi-objective genetic programming-based search across these topologies with SPICE accuracy, to return a set of sized topologies on the optimal performance/yield tradeoff curve. Nonlinear sensitivity analysis, topology decision trees, and analytical tradeoffs. With a data-mining perspective on Pareto-optimal topologies, this book shows how to do global nonlinear sensitivity analysis on topology and sizing variables, automatically extract a specs-to-topology decision tree, and determine analytical expressions of performance tradeoffs. Novel topology design. The MOJITO-N and ISCLEs tools generate novel yet trustworthy topologies; including boosting digitally-sized circuits for analog functionality |
|---|---|
| 記述事項: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9789048129065 |
| アクセス: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |