Effectively Using Machine Learning to Expedite System Level Test Failure Debug

摘要

In this contribution, a machine learning based algorithm to classify system level test failures is proposed. A system level test failure is first modeled as a point in a multidimensional feature space. Then, such failure is classified into a pre-determined failure class, using the multi-class Support Vector Machine classifier, via the one-versus-one approach. When the proposed algorithm is automatically applied to a population of failing system level test failures, defect part per million failure trends can be produced, and used to prioritize debug activities. The proposed algorithm was successfully implemented in the latest Intel