In this paper the Markov chain model of linear feedback shift-registers (LFSR's) for signature analysis testing is analytically solved to obtain the exact expression of the aliasing error probability as a function of test length, error probability, and structure of the feedback network. The dependence on feedback configuration is explored in depth, and it is proven that maximum-length LFSR's (ML-LFSR's) have the best performances with respect to aliasing, regardless of the particular structure of their feedback network. Simplified expressions of aliasing probability are also derived for use as practical tools to design LFSR's for IC-s signature analysis testing, and a heuristic criterion is given for the identification of peaks in aliasing probability. © 1989 IEEE
An Analytical Model for the Aliasing Probability in Signature Analysis Testing
OLIVO, Piero;
1989
Abstract
In this paper the Markov chain model of linear feedback shift-registers (LFSR's) for signature analysis testing is analytically solved to obtain the exact expression of the aliasing error probability as a function of test length, error probability, and structure of the feedback network. The dependence on feedback configuration is explored in depth, and it is proven that maximum-length LFSR's (ML-LFSR's) have the best performances with respect to aliasing, regardless of the particular structure of their feedback network. Simplified expressions of aliasing probability are also derived for use as practical tools to design LFSR's for IC-s signature analysis testing, and a heuristic criterion is given for the identification of peaks in aliasing probability. © 1989 IEEEI documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
