Fault diagnosis of VLSI circuits with cellular automata based pattern classifier

This paper reports a fault diagnosis scheme for very large scale integrated (VLSI) circuits. A special class of cellular automata (CA) referred to as multiple attractor CA (MACA) is employed for the design. State transition behavior of MACA has been analyzed to build a model that can efficiently classify the test responses of a VLSI circuit to diagnose its faulty subcircuit. The MACA-based model, in effect, provides an implicit storage for voluminous test response data and replaces the traditional fault dictionary used for diagnosis of VLSI circuits. The proposed diagnosis scheme employs significantly lesser memory to store the MACA parameters and performs faster diagnosis. Experimental results establish the efficiency of the model in respect of memory overhead, execution speed and percentage of diagnosis.