Multiple distributions for biased random test patterns

The test of integrated circuits by random patterns is very attractive, since no expensive test pattern generation is necessary and tests can be applied with a self-test technique or externally using linear feedback shift registers. Unfortunately, not all circuits are random-testable, because either the fault coverage is too low or the required test length too large. In many cases the random test lengths can be reduced by orders of magnitude using weighted random patterns. However, there are also some circuits for which no single optimal set of weights exists. A set of weights defines a distribution of the random patterns. It is shown that the problem can be solved using several distributions instead of a single one, and an efficient procedure for computing the optimized input probabilities is presented. If a sufficient number of distributions is applied, then all combinational circuits can be tested randomly with moderate test lengths. The patterns can be produced by an external chip, and an optimized test schedule for circuits with a scan path can be obtained. Formulas are derived to determine strong bounds on the probability of detecting all faults