Aliasing and Diagnosis Probability in MISR and STUMPS Using a General Error Model

A number of methods have been proposed to study aliasing in MISR compression. However, most of the methods can compute aliasing probability only for specific test lengths and/or specific error models. Recently, a GLFSR structure [15] was introduced which admits coding theory formulation. The conventional signature analyzers such as LFSR and MISR form special cases of this GLFSR structure. Using this formulation, a general result is now presented which computes the exact aliasing probability for MISRs with primitive feedback polynomials, for any test length and for any error model. The framework is then extended to study the probability of correct diagnosis when faulty signature is used to identify the faulty CUT in the STUMPS environment. Specifically, the results in [7, 15, 161 are extended by proposing two new error models, a general error model which subsumes all the commonly used models, and a fixed magnitude error model which is shown to be useful for fault diagnosis. It is shown how statistical simulation can be used to determine the general error model, for a given CUT. Aliasing for some benchmark circuits, for various error models and test lengths is studied.