Tests for detecting faults in analog and mixed-signal circuits have been traditionally derived from the datasheet speci and #64257;cations. Although these speci and #64257;cations describe important aspects of the device, in many cases these application oriented tests are costly to implement and are inefficient in determining product quality. Increasingly, the gap between speci and #64257;cation test requirements and the capabilities of test equipment has been widening. In this work, a systematic method to generate and evaluate alternate tests for detecting parametric faults is proposed. We recognize that certain aspects of analog test generation problem are not amenable to automation. Additionally, functional features of analog circuits are widely varied and cannot be assumed by the test generator. To overcome these problems, an extended device under test (DUT) model is developed that encapsulates the DUT and the DUT speci and #64257;c tasks. The interface of this model provides a well de and #64257;ned and uniform view of a large class of devices. This permits several simpli and #64257;cations in the test generator. The test generator is uses a search-based procedure that requires evaluation of a large number of candidate tests. Test evaluation is expensive because of complex fault models and slow fault simulation techniques. A tester-resident test evaluation technique is developed to address this issue. This method is not limited by simulation complexity nor does it require an explicit fault model. Making use of these two developments, an efficient and automated test generation method is developed. Theoretical development and a number of examples are used to illustrate various concepts that are presented in this thesis.

La part dû au test dans le coût de conception et de fabrication des circuits intégrés ne cesse de croître, d'où la nécessité d'optimiser cette étape devenue incontournable. Dans cette thèse, de nouvelles méthodes d'ordonnancement et de réduction du nombre de tests à effectuer sont proposées. La solution est un ordre des tests permettant de détecter au plus tôt les circuits défectueux, qui pourra aussi être utilisé pour éliminer les tests redondants. Ces méthodes de test sont basées sur la modélisation statistique du circuit sous test. Cette modélisation inclus plusieurs modèles paramétriques et non paramétrique permettant de s'adapté à tous les types de circuit. Une fois le modèle validé, les méthodes de test proposées génèrent un grand échantillon contenant des circuits défectueux. Ces derniers permettent une meilleure estimation des métriques de test, en particulier le taux de défauts. Sur la base de cette erreur, un ordonnancement des tests est construit en maximisant la détection des circuits défectueux au plus tôt. Avec peu de tests, la méthode de sélection et d'évaluation est utilisée pour obtenir l'ordre optimal des tests. Toutefois, avec des circuits contenant un grand nombre de tests, des heuristiques comme la méthode de décomposition, les algorithmes génétiques ou les méthodes de la recherche flottante sont utilisées pour approcher la solution optimale. / The share of test in the cost of design and manufacture of integrated circuits continues to grow, hence the need to optimize this step. In this thesis, new methods of test scheduling and reducing the number of tests are proposed. The solution is a sequence of tests for early identification of faulty circuits, which can also be used to eliminate redundant tests. These test methods are based on statistical modeling of the circuit under test. This model included several parametric and non-parametric models to adapt to all types of circuit. Once the model is validated, the suggested test methods generate a large sample containing defective circuits. These allow a better estimation of test metrics, particularly the defect level. Based on this error, a test scheduling is constructed by maximizing the detection of faulty circuits. With few tests, the Branch and Bound method is used to obtain the optimal order of tests. However, with circuits containing a large number of tests, heuristics such as decomposition method, genetic algorithms or floating search methods are used to approach the optimal solution.