Analog Performance Prediction Based on Archimedean Copulas Generation Algorithm - Université de Bretagne Occidentale Accéder directement au contenu
Communication Dans Un Congrès Année : 2011

Analog Performance Prediction Based on Archimedean Copulas Generation Algorithm

Résumé

Testing analog circuits is a complex and very time consuming task. In contrary to digital circuits, testing analog circuits needs different configurations, each of them targets a certain set of output parameters which are the performances and the test measures. One of the solutions to simplify the test task and optimize test time is the reduction of the number of to-be-tested performances by eliminating redundant ones. However, the main problem with such a solution is the identification of redundant performances. Traditional methods based on calculation of the correlation between different performances or on the defect level are shown to be not sufficient. This paper presents a new method based on the Archimedean copula generation algorithm. It predicts the performance value from each output parameter value based on the dependence (copula) between the two values. Therefore, different performances can be represented by a single output parameter; as a result, less test configurations are required. To validate the proposed approach, a CMOS imager with two performances and one test measure is used. The simulation results show that the two performances can be replaced by a single test measure. Industrial results are also reported to prove the superiority of the proposed approach.
Fichier principal
Vignette du fichier
idt_2011_beznia.pdf (2.85 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00657531 , version 1 (06-02-2012)

Identifiants

  • HAL Id : hal-00657531 , version 1

Citer

Kamel Beznia, Ahcène Bounceur, Reinhardt Euler. Analog Performance Prediction Based on Archimedean Copulas Generation Algorithm. International Design an Test Workshop, Dec 2011, Beyrouth, Lebanon. ⟨hal-00657531⟩
500 Consultations
221 Téléchargements

Partager

Gmail Facebook X LinkedIn More