Analog Performance Prediction Based on Archimedean Copulas Generation Algorithm

International audienceTesting 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

Outil de modélisation et de génération d'un échantillon multidimensionnel des pixels d'un imageur CMOS

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Génération et optimisation de vecteurs de test analogiques multifréquences

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Parametric test metrics estimation using non-Gaussian copulas

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Evaluation d'un BIST d'un capteur de vision CMOS à base d'une copule non Gaussienne

National audienceL'évaluation des techniques de test analogique/RF nécessite une précision du modèle statistique multivarié des paramètres de sorties du dispositif sous test, à savoir les performances et les mesures de test. Un modèle basé sur les copules sépare les dépendances entre ces paramètres de sortie et leurs distributions marginales, fournissant un ensemble complet permettant de modéliser les dépendances en utilisant des lois statistiques multivariées usuelles. Dans cet article, nous utiliserons la théorie des copules pour estimer un tel modèle et nous montrerons comment utiliser les copules Archimédiennes afin de modéliser des dépendances non Gaussiennes. En particulier, la copule de Clayton sera utilisée pour modéliser les dépendances entre les paramètres de sorties d'un capteur de vision CMOS dans le but d'évaluer son BIST

Accurate Estimation of Analog Test Metrics With Extreme Circuits

International audienceSpecification-based testing of analog/RF circuits is very costly due to lengthy test times and highly sophisticated test equipment. Alternative test measures, extracted by means of Built-In Test (BIT) techniques, are a promising approach to replace standard specification-based tests. However, these test measures must be evaluated at the design stage, before the real production, by estimating parametric test errors such as Test Escapes (TE) and Yield Loss (YL). An accurate estimation of these metrics requires a large non-biased sample of circuit instances including parametric defective ones. Since these extreme circuits are rare events, they cannot be obtained with a Monte Carlo simulation of an affordable size. However, statistical learning techniques, in combination with Monte Carlo simulation, can allow the generation of such a sample for multivariate test metrics estimation. In this paper, we will demonstrate this technique for the evaluation of an RF LNA BIT technique for which a large database of 106 circuits has been simulated for comparison purposes

Accurate Estimation of Analog Test Metrics With Extreme Circuits

International audienceSpecification-based testing of analog/RF circuits is very costly due to lengthy test times and highly sophisticated test equipment. Alternative test measures, extracted by means of Built-In Test (BIT) techniques, are a promising approach to replace standard specification-based tests. However, these test measures must be evaluated at the design stage, before the real production, by estimating parametric test errors such as Test Escapes (TE) and Yield Loss (YL). An accurate estimation of these metrics requires a large non-biased sample of circuit instances including parametric defective ones. Since these extreme circuits are rare events, they cannot be obtained with a Monte Carlo simulation of an affordable size. However, statistical learning techniques, in combination with Monte Carlo simulation, can allow the generation of such a sample for multivariate test metrics estimation. In this paper, we will demonstrate this technique for the evaluation of an RF LNA BIT technique for which a large database of 106 circuits has been simulated for comparison purposes

A Tool for Statistical Modelling by Means of Copulas of Analog and Mixed-Signal Circuits

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