Fault Coverage Estimation for Non-Random Functional Input Sequences

TL;DR: A significantly improved coverage estimation algorithm is presented, modified in order to eliminate the non-random biasing of the input sequence and estimate the detection probabilities of the remaining faults.

Abstract: Statistical stuck-at fault coverage estimation assumes that signals at primary inputs and at other internal gates of the circuit are statistically independent. While valid for random and pseudo-random inputs, this causes substantial errors in coverage estimation for input sequences that are functional and not random, as shown by experimental data presented in this paper. At internal gates, signal correlation due to fanout reconvergence, even for random input sequences, contributes to errors. A significantly improved coverage estimation algorithm is presented in this paper. First, during logic simulation we identify faults that are guaranteed to stay undetected by the applied vectors. Then, after logic simulation, we estimate the detection probabilities of the remaining faults. Compared to Stafan, the statistics gathered during logic simulation are modified in order to eliminate the non-random biasing of the input sequence. Besides the improved detection probabilities, a newly defined effective length (Neff) of the vector sequence corrects for the temporally correlated signals. Experimental results for ISCAS combinational benchmarks demonstrate validity of this approach