Dynamic Search-Space Pruning Techniques in Path Sensitization

A powerful combinational path sensitization engine is required for the efficient implementation of tools for test pattern generation, timing analysis, and delay fault testing. Path sensitization can be posed as a search, in the n-dimensional Boolean space, for a consistent assignment of logic values to the circuit nodes which also satisfies a given condition. In this paper we propose and demonstrate the effectiveness of several new techniques for search-space pruning for test pattern generation. In particular, we present linear-time algorithms for dynamically identifying unique sensitization points and for dynamically maintaining reduced head line sets. In addition, we present two powerful mechanisms that drastically reduce the number of backtracks: failure-driven assertions and dependency-directed backtracking. Both mechanisms can be viewed as a form of learning while searching and have analogs in other application domains. These search pruning methods have been implemented in a generic path sensitization engine called LEAP. A test pattern generator, TG-LEAP, that uses this engine was also developed. We present experimental results that compare the effectiveness of our proposed search pruning strategies to those of PODEM, FAN, and SOCRATES. In particular, we show that LEAP is very efficient in identifying undetectable faults and in generating tests for difficult faults.