A practical approach to comprehensive system test &amp;#x00026; debug using boundary scan based test architecture

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

et al. 2016

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 minimumredundancy-maximum-relevance (mRM R) 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.

Section: Introductionmentioning

confidence: 99%

Efficient Board-Level Functional Fault Diagnosis With Missing Syndromes

Jin

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

et al. 2016

“…The operating frequencies of high-speed chips on a board can be in the range of several GHz, and high-speed I/O rates are also in the GHz range [2]. Due to the prevalence of high-density and high-frequency designs in current boards, defectsper-million (DPM) rates continue to increase and subtle functional failures are becoming increasingly difficult to detect and diagnose for root-cause identification [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Chakrabarty

et al. 2016

Board-level functional fault diagnosis is needed for high-volume production to improve 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 test execution and analysis. We propose an adaptive diagnosis method based on incremental decision trees. Faulty components are classified according to the discriminative ability of the syndromes in decision-tree (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 used in the next step is determined based on the observation of syndromes thus far in the diagnosis procedure. The number of syndromes required for diagnosis can be significantly reduced compared to the total number of syndromes used for system training. Moreover, online learning is facilitated in the proposed diagnosis system using an incremental version of DTs, so as to bridge the knowledge obtained at test-design stage with the knowledge gained during volume production. The diagnosis system can thus adapt to occurrences of new error scenarios on-the-fly. Diagnosis results for three complex boards from industry, currently in volume production, highlight the effectiveness of the proposed approach.

“…The prevalence of high-density, complex, and highfrequency designs in today's printed-circuit boards is resulting in defects that are subtle and difficult to diagnosis using traditional techniques [1]- [3]. When faulty boards are returned to the service center for repair, debug technicians attempt to detect and diagnose failures early in the test process (e.g., through component and structural tests).…”

Section: Introductionmentioning

confidence: 99%

Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Jin

2013 22nd Asian Test Symposium

et al. 2013

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 technologies are based on historical data 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, based on supportvector machines, that can handle missing syndromes by using the method of imputation. Several imputation methods are discussed and compared in terms of their efficiency in handling missing syndromes. 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.