Applying Machine Learning to Testing and Diagnosis of Integrated Systems

The growing complexity of integrated boards and systems makes manufacturing test and diagnosis increasingly expensive. There is a pressing need to reduce test cost and to pinpoint the root causes of integrated systems in a more effective way. In light of machine learning, a number of intelligent test-cost reduction and root-cause analysis methods have been proposed. However, it remains extremely challenging to (i) reduce test cost for black-box testing for integrated systems, and (ii) pinpoint the root causes for integrated systems with little need on labeled test data from repair history. To tackle these challenges, we propose multiple machine-learning-based solutions for black-box test-cost reduction and unsupervised/semi-supervised root-cause analysis in this dissertation.For black-box test-cost reduction, we propose a novel test selection method based on a Bayesian network model. First, it is formulated as a constrained optimization problem. Next, a score-based algorithm is implemented to construct the Bayesian network for black-box tests. Finally, we propose a Bayesian index with the property of Markov blankets, and then an iterative test selection method is developed based on our proposed Bayesian index. For root-cause analysis, we first propose an unsupervised root-cause analysis method in which no repair history is needed. In the first stage, a decision-tree model is trained with system test information to cluster the data in a coarse-grained manner. In the second stage, frequent-pattern mining is applied to extract frequent patterns in each decision-tree node to precisely cluster the data so that each cluster represents only a small number of root causes. The proposed method can accommodate both numerical and categorical test items. A combination of the L-method, cross validation and Silhouette score enables us to automatically determine all hyper-parameters. Two industry case studies with system test data demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised root-cause-analysis method. Utilizing transfer learning, we further improve the performance of unsupervised root-cause-analysis. A two-stage clustering method is first developed by exploiting model selection based on the concept of Silhouette score. Next, a data-selection method based on ensemble learning is proposed to transfer valuable information from a source product to improve the diagnosis accuracy on the target product with insufficient data. Two case studies based on industry designs demonstrate that the proposed approach significantly outperforms other state-of-the-art unsupervised root-cause-analysis methods. In addition, we propose a semi-supervised root-cause-analysis method with co-training, where only a small set of labeled data is required. Using random forest as the learning kernel, a co-training technique is proposed to leverage the unlabeled data by automatically pre-labeling a subset of them and retraining each decision tree. In addition, several novel techniques have been proposed to avoid over-fitting and determine hyper-parameters. Two case studies based on industrial designs demonstrate that the proposed approach significantly outperforms the state-of-the-art methods. In summary, this dissertation addresses the most difficult problems in testing and diagnosis of integrated systems with machine learning. A test selection method based on Bayesian networks reduces the test cost for black-box testing. With unsupervised learning, semi-supervised learning and transfer learning, we analysis root causes for integrated systems without much need on historical diagnosis information. The proposed approaches are expected to contribute to the semiconductor industry by effectively reducing the black-box test cost and efficiently diagnosing the integrated systems.