Optimization-Based Design of Nano-CMOS LC-VCOs - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Optimization-Based Design of Nano-CMOS LC-VCOs

Résumé

This paper introduces a variability-aware methodology for the design of LC-VCOs in Nano-CMOS technologies. The complexity of the design as well as the necessity for having an environment offering the possibility for exploring design trade-offs has led to the development of design methodologies based multi-objective optimization procedures yielding the generation of Pareto-optimal surfaces. The efficiency of the process is granted by using analytical models for both passive and active devices. Although physics-based analytical expressions have been proposed for the evaluation of the lumped elements, the variability of the process parameters is usually ignored due to the difficulty to formalize it into an optimization performance index. The usually adopted methodology of considering only optimum solutions for the Pareto surface, may lead to pruning quasi-optimal solutions that may prove to be better, should their sensitivity to process parameter variation be accounted for. In this work we propose starting by generating an extended Pareto surface where both optimum and quasi-optimum solutions are considered. Finally information on the sensitivity to process parameter variations, is used for electing the best design solution.
Fichier principal
Vignette du fichier
978-3-642-28255-3_50_Chapter.pdf (4 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01365759 , version 1 (13-09-2016)

Licence

Paternité

Identifiants

Citer

Pedro Pereira, Helena Fino, Fernando V. Coito, M. Ventim-Neves. Optimization-Based Design of Nano-CMOS LC-VCOs. 3rd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2012, Costa de Caparica, Portugal. pp.453-464, ⟨10.1007/978-3-642-28255-3_50⟩. ⟨hal-01365759⟩
373 Consultations
185 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More