PROJECT TITLE :
Step-Down Spatial Randomness Test for Detecting Abnormalities in DRAM Wafers with Multiple Spatial Maps
Defects on semiconductor wafers don't seem to be uniformly distributed, however tend to cluster. These spatial defect patterns contain useful data concerning issues during integrated circuit fabrication. Promptly detecting abnormal wafers is a crucial manner to increase yield and products quality. However, research on identifying spatial defect patterns has targeted only on flash memory with a single wafer map. No procedure is accessible for identifying spatial defect patterns on dynamic random access memory (DRAM) with multiple wafer maps. This paper proposes a new step-down spatial randomness check for detecting abnormalities on a DRAM wafer with multiple spatial maps. We adopt nonparametric Gaussian kernel-density estimation to transform the original fail bit take a look at (FBT) values into binary FBT values. We also propose a spatial native de-noising methodology to eliminate noisy defect chips to distinguish the random defect patterns from systematic ones. We have a tendency to experimentally validated the proposed procedure using real-life DRAM wafers. These experimental results demonstrate that our approach will viably replace manual detection of abnormal DRAM wafers.
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