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A new technique for evaluating I D D p data using a clustering bdsed approach is presented. While prevailing IDDQ test tedhniques rely on a jxed threshold or the current signature of an IC, the proposed technique relies on abnormalities of the I D D p distribution of a device with respect to other devices in the test set. Results of applying this technique to data collected on a high volume graphics chip are described. Results are also compared to 'rhe conventional single threshold approach, and benefits #f the new technique are presented.

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Effectiveness of the clustering based approach in detecting devices with abnormal I DDQ values is evaluated using data from the SEMATECH test methods experiment. The results from clustering are compared to the results obtained on actual silicon during the SEMATECH study. The differences between the results obtained in each case are analyzed. The clustering approach is also compared to two common I DDQ test techniques, the single-threshold approach and the delta-I DDQ approach, and the results are presented.