Recursive Learning: An attractive alternative to the decision tree for test generation in digital ci

Abstract

Most test generators for combinational and sequential circuits use a branch and bound technique in order to systematically explore the search space when trying to generate a test vector. This paper presents an alternative method. Instead of using a decision tree to implicitly try all combinations of signal values for a given set of signals we use a learning routine which can be called recursively. Given enough recursions, it is guaranteed that we can identify all necessary assignments at a given stage of the algorithm. Our method is general in the sense that it can be combined with any logic alphabet and can be integrated in any FAN- based test generator for combinational circuits. Furthermore, recursive learning is equally applicable for test generation in sequential circuits and can even be used in hierarchical approaches. We show experimental results that demonstrate the attractiveness of our approach by comparing recursive learning with the conventional branch and bound technique for test generation in combinational circuits.