As process variations increase and devices get more diverse in their behavior, using the same test list for all devices is increasingly inefficient. Methodologies that adapt the test sequence with respect to lot, wafer, or even a device's own behavior help contain the test cost while maintaining test quality. In adaptive test selection approaches, the initial test list, a set of tests that are applied to all devices to learn information, plays a crucial role in the quality outcome. Most adaptive test approaches select this initial list based on fail probability of each test individually. Such a selection approach does not take into account the correlations that exist among various measurements and potentially will lead to the selection of correlated tests. In this work, we propose a new adaptive test algorithm that includes a mathematical model for initial test ordering that takes correlations among measurements into account. The proposed method can be integrated within an existing test flow running in the background to improve not only the test quality but also the test time. Experimental results using four distinct industry circuits and large amounts of measurement data show that the proposed technique outperforms prior approaches considerably. CCS Concepts: • Hardware → Analog, mixed-signal and radio frequency test;

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