GPU Based Test Selection on Maximizing 1-to-n Detection Fault Coverage

Previous test selection methods are based on serial algorithms which cannot meet the continuously increasing CPU runtime and test data volume.This paper proposes a Graphic Processing Unit (GPU) based Max 1-to-n detection test selection method to produce a high quality test set from any possible pattern sources, either generated randomly or deterministically.We adapt GPU to parallelize the test selection which considers the limitation of the test cost and select the tests that could maximize the 1-to-n detection fault coverage.The tests are ordered by their contribution to ndetection fault coverage so that they can detect more defected chips in the early time of test application and reduce the test cost.Experiments demonstrate the proposed method offers a 21.9X speedup compared with the previous n-detecion-aware test selection method.Moreover, in comparison with the test set generated by commercial tools, under the same test set size, the selected test set produced by the proposed method could achieve a better n-detection fault coverage (3.2%~8.3%improvement on average) and a steeper fault coverage curve.