AdaTest: An efficient statistical test framework for test escape screening

Statistical analyses based on production test data can help identify test escapes, which are chips that pass the test program but fail later at system-level test or in field. Such analyses do not require extra physical measurements and can be referred to as statistical tests. For designing effective statistical tests, this paper investigates the use of a learning framework based on Adaptive Boosting, which has demonstrated great success in real-time face and object recognition. The framework is composed of a cascade of AdaBoost classifiers, each of which uses a small set of most relevant features that are automatically selected in the training phase, to identify a subset of test escapes. This framework therefore generates only the features that are most relevant for classification and significantly reduces the runtime and memory usage for statistical tests during test application. We also propose a new feature set to characterize the chips under test and demonstrate that including the new feature set as input to the proposed feature selection framework could reveal more test escapes.