Maintaining Accuracy of Test Compaction through Adaptive Re-learning

In test compaction, the objective is to reduce cost of testing an integrated system by applying a subset of its specification-based tests. One approach for accomplishing this objective is to statistically learn a correlation function for the tests eliminated from a collection of systems that are fully tested (i.e., training data). Accuracy of this correlation function may degrade over the life span of an integrated system however. We describe an adaptive scheme that (1) uses stratified sampling to check the accuracy of a correlation function at various time instances and (2) re-learns a function when its accuracy dips below some tolerable threshold. This methodology is applied to test data from two in-production integrated systems, namely, an accelerometer and a phase-locked loop. Experiments that use over 200,000 real chips demonstrate that the difference between actual function accuracy and its estimate using stratified sampling is smaller than 2%. Moreover, our adaptive re-learning is able to improve the accuracy for one out of three accelerometer and one out of three PLL sampling instances where the function accuracy was unacceptable.