Fault Diagnosis with Orthogonal Compactors in Scan-Based Designs

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

Chakrabarty

et al. 2015

Reasoning-based functional-fault diagnosis has recently been advocated to achieve high diagnosis accuracy, low defect escapes, and reducing manufacturing cost. However, such diagnosis method requires a rich set of test items (syndromes) and a sizable database of faulty boards to learn from. An insufficient number of failed boards, ambiguous root-cause identification, and redundant or irrelevant syndromes can render reasoningbased diagnosis ineffective. Periodic evaluation and analysis can help locate weaknesses in a diagnosis system and thereby provide guidelines for redesigning the tests, which facilitates better diagnosis. We propose an information-theoretic framework for evaluating the effectiveness of and providing guidance to a reasoning-based functional-fault diagnosis system. Syndrome analysis based on feature selection methods provides a representative set of syndromes and suggests irrelevant syndromes in diagnosis. Root-cause analysis measures the discriminative ability of differentiating a given root cause from others. Results are presented for four types of diagnosis systems for three complex boards that are in volume production.

Section: Illustration Of Syndrome Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

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

Chakrabarty

et al. 2015

“…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). A defect's most probable cause, logic location, and even physical location can sometimes be determined based on scan test [3]. However, traditional structural test techniques are not sufficient to guarantee product quality.…”

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

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.

“…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). A defect's most probable cause, logic location, and even physical location can sometimes be determined based on scan test [4]. However, traditional structural test techniques are not sufficient to guarantee product quality.…”

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.