Design of multiple attractor GF(2p) cellular automata for diagnosis of VLSI circuits

This paper introduces an efficient diagnosis scheme for VLSI circuits. A special class of non-group CA referred to as multiple attractor cellular automats (MACA) is introduced to diagnose the faulty block of a circuit under test (CUT). The scheme employs significantly lesser memory than the existing methods reported so far. Experimental results establish the efficiency of the scheme in terms of saving in memory space and execution time and enhanced diagnostic resolution. Rather than GF(2) CA where each CA cell handles GF(2) elements (0 and 1), the GF(2^p) CA is employed to reduce the processing time