Unknown values emerge during the design and test generation process as well as during later test application and system operation. They adversely affect the test quality by reducing the controllability and observability of internal circuit structures - resulting in a loss of fault coverage. To handle unknown values, conventional test generation algorithms as used in state-of-the-art commercial tools, rely on n-valued algebras. However, n-valued algebras introduce pessimism as soon as X-values reconverge. Consequently, these algorithms fail to compute the accurate result. This paper focuses on a new highly incremental CEGAR-based algorithm that overcomes these limitations and hence is completely accurate in presence of unknown values. It relies on a modified SAT-solver tailored for this specific problem. The experimental results for circuits with up to 2 400 000 gates show that this combination allows high accuracy and high scalability at the same time. Compared to a state-of-the-art commercial tool, the fault coverage could be increased significantly. Furthermore, the runtime is reduced remarkably compared to a QBF-based encoding of the problem.
['Karsten Scheibler', 'Dominik Erb', 'Bernd Becker']
Accurate CEGAR-based ATPG in presence of unknown values for large industrial designs