A variability-aware adaptive test flow for test quality improvement

In this paper, we propose a process-variability-aware adaptive test flow that realizes efficient and comprehensive detection of parametric faults. A parametric fault is essentially a malfunction in a large-scale integration chip, which is caused by the variability in fabrication processes. In our adaptive test framework, test pattern sets are altered on individual chips in order to apply the optimal set of test patterns for each chip, and thus the test coverage is improved and the test time is reduced. The test pattern is chosen on the basis of parameter estimations measured using an on-chip sensor with respect to statistical timing information. We also propose a novel metric to quantize the test coverage suitable for evaluating the test quality of parametric faults. Our experimental results using an industrial design show that the proposed flow significantly improves the parametric fault coverage and test efficiency compared to conventional test flows.