An Efficient Diagnostic Test Pattern Generation Framework Using Boolean Satisfiability

This paper presents a diagnostic test pattern generation (DTPG) framework based upon a Boolean Satisfiability engine. We first propose an enhanced miter-based model for distinguishing fault candidates that can achieve greater efficiency as well as can prove a group of undifferentiable faults. The model can also be used to generate diagnostic tests for distinguishing faults of different fault types. Based on this model, we propose a diagnostic pattern compaction strategy. By exploring "don't cares " at the primary inputs, the number of required diagnostic patterns can be reduced. Experimental results show that the proposed method achieves a greater diagnosis resolution when combined with existing approaches. Also, fewer diagnostic test patterns are needed.