Diagnosing scan chain timing faults through statistical feature analysis of scan images

Excessive test mode power-ground noise in nanometer scale chips causes large delay uncertainties in scan chains, resulting in a highly elevated rate of timing failures. The hybrid timing violation types in scan chains, plus their possibly intermittent manifestations, invalidate the traditional assumptions in scan chain fault behavior, significantly increasing the ambiguity and difficulty in diagnosis. In this paper, we propose a novel methodology to resolve the challenge of diagnosing multiple permanent or intermittent timing faults in scan chains. Instead of relying on fault simulation that is incapable of approximating the intermittent fault manifestation, the proposed technique characterizes the impact of timing faults by analyzing the phase movement of scan patterns. Extracting fault-sensitive statistical features of phase movement information provides strong signals for the precise identification of fault locations and types. The manifestation probability of each fault is furthermore computed through a mathematical transformation framework which accurately models the behavior of multiple faults as a Markov chain. The fault model utilized in the proposed scheme considers the effect of possibly asymmetric fault manifestation, thus maximally approximating the realistic failure behavior. Simulations on large benchmark circuits and two industrial designs have confirmed that the proposed methodology can yield highly accurate diagnosis results even for complicated fault manifestations such as multiple intermittent faults with mixed fault types.