Aliasing Errors in Signature in Analysis Registers

Abstract

The authors discuss aliasing errors in signature analysis registers for self-testing networks and review analytical results. The results show that when p, the probability that an error will occur at a network output, is close to 1/2, there is a bound of the aliasing error. The analysis uses a graph to represent the probability of transition, the Markov process, and z-transforms to analyze the behavior of the signature analysis register. For very small p (p-0) and very large p (p-1), the aliasing error solution for primitive polynomials is a series of terms (1-)' in magnitude (where n is the number of random patterns being applied to the network or the length of the network output sequence). As compared with nonprimitive polynomials, whose solution is n(1-)n or n2(1-)p, in general primitive polynomials are much better with respectto aliasing. Simulation results are shown for aliasing errors for these polynomials, which give insight as to how aliasing occurs.

A Signature Analysis Technique for the Identification of Failing Vectors with Application to Scan-BIST*

“…The responses are then captured by the scan chains and compacted to a short signature in a multiple-input signature register (MISR) [3]. The properties of MISRs and their effectiveness for response compaction have been studied extensively in the literature [12,18,21,25,28]. However, a drawback of this approach is that the compact signature provided by the MISR provides only limited information for fault diagnosis, either to determine (failing) test vectors that produce errors on observable outputs or to identify errorcapturing (failing) scan cells.…”

Cad Tools For Bist Insertion And Analysis

“…To clarify this further, consider the polynotnial P(x) of (2.1) again. This polynomial has n coefficients %.l thrcugh %* Assuming the Equiprobable Error Model, all error patterns are equally likely and each error pattern is as likely to occur as any other error pattern [4,5]. Under this assumption, each of %-1 through coefficient can either be a 0 or 1 independently.…”

Aliasing errors in signature analysis testing of integrated circuits

“…The bits e; (whose probability of being 1 will be denoted with p ) are here assumed to be statistically independent, so that the future state of a LFSR depends only on the current error bit and state. The register behavior can thus be represented as a discrete-time Markov process, whose states and transitions correspond to those of the LFSR [2,3]. This type of stochastic processes is fully described by the transition matrix M = [m;,j], whose elements m,,j are the probability of a transition from state j to state i in one step.…”

“…As the probability of being in state-0 a t n is given by TO(^), from the definition of AEP(n) it follows that AEP(n) = TO(^) -(1 -p)" , ( 2 ) where the last term gives the probability of the trivial all-zero sequence, that cannot be considered as producing aliasing 131.…”