A lightweight N-cover algorithm for diagnostic fail data minimization
The increasing design complexity of modern ICs has made it extremely difficult and expensive to test them comprehensively. As the transistor count and density of circuits increase, a large volume of fail data is collected by the tester for a single failing IC. The diagnosis procedure analyzes this fail data to give valuable information about the possible defects that may have caused the circuit to fail. However, without any feedback from the diagnosis procedure, the tester may often collect fail data which is potentially not useful for identifying the defects in the failing circuit. This not only consumes tester memory but also increases tester data logging time and diagnosis run time. In this work, we present an algorithm to minimize the amount of fail data used for high quality diagnosis of the failing ICs. The developed algorithm analyzes outputs at which the tests failed and determines which failing tests can be eliminated from the fail data without compromising diagnosis accuracy. The proposed algorithm is used as a preprocessing step between the tester data logs and the diagnosis procedure. The performance of the algorithm was evaluated using fail data from industry manufactured ICs. Experiments demonstrate that on average, 43% of fail data was eliminated by our algorithm while maintaining an average diagnosis accuracy of 93%. With this reduction in fail data, the diagnosis speed was also increased by 46%.
Pomeranz, Purdue University.
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