Compactor Independent Direct Diagnosis
ATS '04 Proceedings of the 13th Asian Test Symposium
Specification Test Compaction for Analog Circuits and MEMS
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Compaction of pass/fail-based diagnostic test vectors for combinational and sequential circuits
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Statistical Test Compaction Using Binary Decision Trees
IEEE Design & Test
Similarity Learning for Nearest Neighbor Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Two Phase Approach for Minimal Diagnostic Test Set Generation
ETS '09 Proceedings of the 2009 European Test Symposium
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
A Formal Condition to Stop an Incremental Automatic Functional Diagnosis
DSD '10 Proceedings of the 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools
Optimal Test Set Selection for Fault Diagnosis Improvement
DFT '11 Proceedings of the 2011 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Test data collection for a failing integrated circuit (IC) can be very expensive and time consuming. Many companies now collect a fix amount of test data regardless of the failure characteristics. As a result, limited data collection could lead to inaccurate diagnosis, while an excessive amount increases the cost not only in terms of unnecessary test data collection but also increased cost for test execution and data-storage. In this work, the objective is to develop a method for predicting the precise amount of test data necessary to produce an accurate diagnosis. By analyzing the failing outputs of an IC during its actual test, the developed method dynamically determines which failing test pattern to terminate testing, producing an amount of test data that is sufficient for an accurate diagnosis analysis. The method leverages several statistical learning techniques, and is evaluated using actual data from a population of failing chips and five standard benchmarks. Experiments demonstrate that test-data collection can be reduced by 30% (as compared to collecting the full-failure response) while at the same time ensuring 90% diagnosis accuracy. Prematurely terminating test-data collection at fixed levels (e.g., 100 failing bits) is also shown to negatively impact diagnosis accuracy.