Test-Quality/Cost Optimization Using Output-Deviation-Based Reordering of Test Patterns

At-speed functional testing, delay testing, and n-detection test sets are being used today to detect deep submicrometer defects. However, the resulting test data volumes are too high; the 2005 International Roadmap for Semiconductors predicts that test-application times will be 30 times larger in 2010 than they are today. In addition, many new types of defects cannot be accurately modeled using existing fault models. Therefore, there is a need to model the quality of test patterns such that they can be quickly assessed for defect screening. Test selection is required to choose the most effective pattern sequences from large test sets. Current industry practice for test selection is based on fault grading, which is computationally expensive and must also be repeated for every fault model. Moreover, although efficient methods exist today, for fault-oriented test generation, there is a lack of understanding on how best to combine the test sets thus obtained, i.e., derive the most effective union of the individual test sets without simply taking all the patterns for each fault model. This paper presents the use of the output deviation as a surrogate coverage-metric for pattern modeling and test grading. A flexible, but general, probabilistic-fault model is used to generate a probability map for the circuit, which can subsequently be used for test-pattern reordering. The output deviations resulting from the probability map(s) are used as a coverage-metric to model test patterns; the higher the deviation, the better the quality of the test pattern. We show that, for the ISCAS benchmark circuits and as compared to other reordering methods, the proposed method provides ldquosteeperrdquo coverage curves for different fault models.