Pseudorandom test-length analysis using differential solutions

As the size of VLSI circuits increases, the use of random testing is becoming more common. One of the most important aspects of random testing is the determination of the test pattern length that guarantees a high confidence of fault detection. Generally, random test length is estimated by assuming that the set of test patterns applied is purely random. The assumption is not completely correct in applications where linear feedback shift registers (LFSR´s) are employed to generate input vectors. In this paper, we have developed a test (Markov) model which faithfully reflects the pseudorandom behavior of test patterns, and all detectable single stuck-at faults (instead of the worst single stuck-fault only) are considered. The required test length is then determined by solving differential equations to achieve the specified test confidence. Based on the test model, analysis is first dedicated to the two-fault case, results are then extended to the k-fault analysis where k⩾3. The test length thus determined is smaller than that derived based on the random pattern assumption, and test costs can be greatly reduced