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Statistical Test Compaction Using Binary Decision Trees
November/December 2006 (vol. 23 no. 6)
pp. 452-462
Sounil Biswas, Carnegie Mellon University
Ronald D. (Shawn) Blanton, Carnegie Mellon University
Because of the significant cost of explicitly testing an integrated, heterogeneous device for all its specifications, there is a need for a test methodology that minimizes test cost while maintaining product quality and limiting yield loss. The authors are developing a decision-tree-based statistical-learning methodology to compact the complete specification-based test set of an integrated device by eliminating redundant tests. A test is deemed redundant if its output can be reliably predicted using other tests that are not eliminated. To ensure the required accuracy for commercial devices, the authors employ a number of modeling and data-massaging techniques to reduce prediction error. Test compaction results produced for a commercial MEMS accelerometer are promising in that they indicate it is possible to eliminate an expensive mechanical test.
Index Terms:
statistical test compaction, binary decision trees, heterogeneous devices, redundant tests, kept tests, go/no-go testing
Citation:
Sounil Biswas, Ronald D. (Shawn) Blanton, "Statistical Test Compaction Using Binary Decision Trees," IEEE Design & Test of Computers, vol. 23, no. 6, pp. 452-462, Nov.-Dec. 2006, doi:10.1109/MDT.2006.154
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