TY - GEN
T1 - A memory failure pattern analyzer for memory diagnosis and repair
AU - Lin, Bing Yang
AU - Lee, Mincent
AU - Wu, Cheng Wen
PY - 2012/8/20
Y1 - 2012/8/20
N2 - As VLSI technology advances and memories occupy more and more area in a typical SOC, memory diagnosis has become an important issue. In this paper, we propose the Memory Failure Pattern Analyzer (MFPA), which is developed for different memories and technologies that are currently used in the industry. The MFPA can locate weak regions of the memory array, i.e., those with high failure rate. It can also be used to analyze faulty-cell/defect distributions automatically. We also propose a new defect distribution model which has 1-12 times higher accuracy than other theoretical models. Based on this model, we propose a defect-spectrum-based methodology to identify critical failure patterns from failure bitmaps. These failure patterns can further be translated to corresponding defects by our memory fault simulator (RAMSES) and physical-level failure analysis tool (FAME). In an industrial case, the MFPA fits the defect distribution with the proposed model, which has 12 times higher accuracy than the Poisson distribution. With our model, it further identifies two special failure patterns from 132,488 faulty 4-Mb macros in 1.2 minutes.
AB - As VLSI technology advances and memories occupy more and more area in a typical SOC, memory diagnosis has become an important issue. In this paper, we propose the Memory Failure Pattern Analyzer (MFPA), which is developed for different memories and technologies that are currently used in the industry. The MFPA can locate weak regions of the memory array, i.e., those with high failure rate. It can also be used to analyze faulty-cell/defect distributions automatically. We also propose a new defect distribution model which has 1-12 times higher accuracy than other theoretical models. Based on this model, we propose a defect-spectrum-based methodology to identify critical failure patterns from failure bitmaps. These failure patterns can further be translated to corresponding defects by our memory fault simulator (RAMSES) and physical-level failure analysis tool (FAME). In an industrial case, the MFPA fits the defect distribution with the proposed model, which has 12 times higher accuracy than the Poisson distribution. With our model, it further identifies two special failure patterns from 132,488 faulty 4-Mb macros in 1.2 minutes.
UR - http://www.scopus.com/inward/record.url?scp=84865041136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865041136&partnerID=8YFLogxK
U2 - 10.1109/VTS.2012.6231059
DO - 10.1109/VTS.2012.6231059
M3 - Conference contribution
AN - SCOPUS:84865041136
SN - 9781467310741
T3 - Proceedings of the IEEE VLSI Test Symposium
SP - 234
EP - 239
BT - Proceedings - 2012 30th IEEE VLSI Test Symposium, VTS 2012
T2 - 2012 30th IEEE VLSI Test Symposium, VTS 2012
Y2 - 23 April 2012 through 26 April 2012
ER -