Substantial Fault Pairs at-A-Time (SFPAT): An Automatic Diagnostic Pattern Generation Method
Volume diagnosis plays an important role in the yield learning process. To get a high quality diagnosis result, patterns with high distinguishability are essential. However, the test patterns used by volume diagnosis commonly have low distinguishability to specific faults. In our experiments, we observe that on average, under automatic generated test patterns, faults in the same fanout free region (FFR) account for only 6％ of all possible fault pairs, but their share in total indistinguishable faults is 70％; faults in different FFRs but with the same observation points account for 4％ of all fault pairs, but their share in total indistinguishable faults is 22％. Exploiting this fact that faults in the same FFRs are harder to be distinguished, we propose an Automatic Diagnostic Pattern Generation (ADPG) method named Substantial Fault Pairs at-A-Time (SFPAT)-ADPG. By applying a transformed circuit and a new fault list to an existing Automatic Test Pattern Generation (ATPG) tool, we generate the compressed test patterns which are also the diagnostic patterns with high distinguishability for the original circuit. Experiments on ISCAS89 and ITC99 benchmark circuits show the effectiveness of the proposed SFPAT-ADPG method.
fault diagnosis distinguishability automatic test pattern generation automatic diagnostic pattern generation substantial fault pairs at-a-time
Jing Ye Xiaolin Zhang Yu Hu Xiaowei Li
Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Acade Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Acade
国际会议
2010 19th IEEE Asian Test Symposium(第19届IEEE亚洲测试技术学术会议 ATS 2010)
上海
英文
192-197
2010-12-01（万方平台首次上网日期，不代表论文的发表时间）