Zhaobo Zhang

Bio: Zhaobo Zhang is an academic researcher from Huawei. The author has contributed to research in topics: Core router & Anomaly detection. The author has an hindex of 13, co-authored 54 publications receiving 516 citations. Previous affiliations of Zhaobo Zhang include Duke University.

Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting

Abstract: Increasing integration densities and high operating speeds lead to subtle manifestation of defects at the board level. Functional fault diagnosis is, therefore, necessary for board-level product qualification. However, ambiguous diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost and adversely impact yield. Advanced machine-learning (ML) techniques offer an unprecedented opportunity to increase the accuracy of board-level functional diagnosis and reduce high-volume manufacturing cost through successful repair. We propose a smart diagnosis method based on two ML classification models, namely, artificial neural networks (ANNs) and support-vector machines (SVMs) that can learn from repair history and accurately localize the root cause of a failure. Fine-grained fault syndromes extracted from failure logs and corresponding repair actions are used to train the classification models. We also propose a decision machine based on weighted-majority voting, which combines the benefits of ANNs and SVMs. Three complex boards from the industry, currently in volume production, and additional synthetic data, are used to validate the proposed methods in terms of diagnostic accuracy, resolution, and quantifiable improvement over current diagnostic software.

Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

Abstract: Advanced machine learning techniques offer an unprecedented opportunity to increase the accuracy of board-level functional fault diagnosis and reduce product cost through successful repair. Ambiguous or incorrect diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost. We propose a smart diagnosis method based on multikernel support vector machines (MK-SVMs) and incremental learning. The MK-SVM method leverages a linear combination of single kernels to achieve accurate faulty-component classification based on the errors observed. The MK-SVMs thus generated can also be updated based on incremental learning, which allows the diagnosis system to quickly adapt to new error observations and provide even more accurate fault diagnosis. Two complex boards from industry, currently in volume production, are used to validate the proposed diagnosis approach in terms of diagnosis accuracy (success rate) and quantifiable improvements over previously proposed machine-learning methods based on several single-kernel SVMs and artificial neural networks.

Board-level fault diagnosis using Bayesian inference

Abstract: Increasing integration densities and high operating speeds are leading to subtle manifestations of defects at the board level. Board-level functional test is therefore necessary for product qualification. The diagnosis of functional failures is especially challenging, and the cost associated with board-level diagnosis is escalating rapidly. An effective and cost-efficient board-level diagnosis strategy is needed to reduce manufacturing cost and time-to-market, as well as to improve product quality. In this paper, we use Bayesian inference to develop a new board-level diagnosis framework that allows us to identify faulty devices or faulty modules within a device on a failing board with high confidence. Bayesian inference offers a powerful probabilistic method for pattern analysis, classification, and decision making under uncertainty. We apply this inference technique by first generating a database of fault syndromes obtained using fault-insertion test at the module pin level on a fault-free board, and then use this database along with the observed erroneous behavior of a failing board to infer the most likely faulty device. Results on a case study using an open-source RISC system-on-chip highlight the effectiveness of the proposed framework in terms of fault-localization accuracy and correctness of diagnosis.

Efficient Board-Level Functional Fault Diagnosis With Missing Syndromes

Abstract: Functional fault diagnosis is widely used in board manufacturing to ensure product quality and improve product yield. Advanced machine-learning techniques have recently been advocated for reasoning-based diagnosis; these techniques are based on the historical record of successfully repaired boards. However, traditional diagnosis systems fail to provide appropriate repair suggestions when the diagnostic logs are fragmented and some error outcomes, or syndromes, are not available during diagnosis. We describe the design of a diagnosis system that can handle missing syndromes and can be applied to four widely used machine-learning techniques. Several imputation methods are discussed and compared in terms of their effectiveness for addressing missing syndromes. Moreover, a syndrome-selection technique based on the minimum-redundancy-maximum-relevance criteria is also incorporated to further improve the efficiency of the proposed methods. Two large-scale synthetic data sets generated from the log information of complex industrial boards in volume production are used to validate the proposed diagnosis system in terms of diagnosis accuracy and training time.

Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

Abstract: Functional fault diagnosis at board-level is desirable for high-volume production since it improves product yield. However, to ensure diagnosis accuracy and effective board repair, a large number of syndromes must be used. Therefore, the diagnosis cost can be prohibitively high due to the increase in diagnosis time and the complexity of syndrome collection/analysis. We propose an adaptive diagnosis method based on decision trees (DTs). Faulty components are classified according to the discriminative ability of the syndromes in DT training. The diagnosis procedure is constructed as a binary tree, with the most discriminative syndrome as the root and final repair suggestions are available as the leaf nodes of the tree. The syndrome to be collected in the next step is determined based on the observations of syndromes collected thus far in the diagnosis procedure. The number of syndromes required for diagnosis can also be significantly reduced compared to the number of syndromes used for system training. Diagnosis results for two complex boards from industry, currently in volume production, and additional synthetic data highlight the effectiveness of the proposed approach.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Multiple Imputation for Nonresponse in Surveys

Abstract: 25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Statistical Pattern Recognition

Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches

Abstract: This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.