METER: MEasuring Test Effectiveness Regionally
Researchers from both academia and industry continually propose new fault models and test metrics for coping with the ever-changing failure mechanisms exhibited by scaling fabrication processes. Understanding the relative effectiveness of current and proposed metrics and models is vitally important for selecting the best mix of methods for achieving a desired level of quality at reasonable cost. Evaluating metrics and models traditionally relies on actual test experiments, which is time-consuming and expensive. To reduce the cost of evaluating new test metrics, fault models, design-for-test techniques, and others, this paper proposes a new approach, MEeasuring Test Effectiveness Regionally (METER). METER exploits the readily available test-measurement data that is generated from chip failures. The approach does not require the generation and application of new patterns but uses analysis results from existing tests, which we show to be more than sufficient for performing a thorough evaluation of any model or metric of interest. METER is demonstrated by comparing several metrics and models that include: 1) stuck-at; 2) N-detect; 3) PAN-detect (physically-aware N-detect); 4) bridge fault models; and 5) the input pattern fault model (also more recently referred to as the gate-exhaustive metric). We also provide in-depth discussion on the advantages and disadvantages of METER, and contrast its effectiveness with those from the traditional approaches involving the test of actual integrated circuits.
test effectiveness fault models test metrics test evaluation