Learning from Production Test Data: Correlation Exploration and Feature Engineering


Abstract—The huge amount of test data of a modern chip produced during manufacturing test could be mined for valuable information about the device under test (DUT), far more than the pass/fail information of each test item. Exploring the hidden cor-relations and patterns in the test data allows better understanding of the DUT and could therefore lead to test cost reduction or test quality improvement. There are several known types of correlations embedded in the test data: spatial correlations, inter-test-item correlations, and temporal correlations, each of which may involve a large number of data dimensions. Deriving and selecting the most relevant features for a specific application is critical for designing an effective and efficient mining solution. This paper provides an overview of recent research efforts on correlation exploration and development of a framework of feature engineering for learning from production test data. I

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oaioai:CiteSeerX.psu: time updated on 10/29/2017

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