Symbolic optimization of FSM networks based on sequential ATPG techniques

This paper presents a novel optimization algorithm for FSM networks that relies on sequential test generation and redundancy removal. The implementation of the proposed approach, which is based on the exploitation of input don´t care sequences through regular language intersection, is fully symbolic. Experimental results, obtained on a large set of standard benchmarks, improve over the ones of state-of-the-art methods