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Diagnosis Using Support Vector Machines (SVM)

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Abstract

Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to cope with high complexity and ever-increasing clock frequencies, and the identification of the root cause of failure on a board is a major problem today. Ambiguous or incorrect repair suggestions lead to long debug times and even wrong repair actions, which significantly increases the repair cost and adversely impacts yield.

Keywords

Board-level Fault diagnosis Functional failures Incremental learning Kernel Machine learning Production Support vector machines 

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Huawei TechnologiesSanta ClaraUSA
  2. 2.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA

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