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공공누리This item is licensed Korea Open Government License

dc.contributor.author
김병훈
dc.contributor.author
동승훈
dc.contributor.author
장인갑
dc.contributor.author
정명기
dc.contributor.author
정영선
dc.date.accessioned
2019-08-28T07:41:51Z
dc.date.available
2019-08-28T07:41:51Z
dc.date.issued
2016-02-01
dc.identifier.issn
0894-6507
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/14499
dc.identifier.uri
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART75084962
dc.description.abstract
Defects on semiconductor wafers are not uniformly distributed, but tend to cluster. These spatial defect patterns contain useful information about issues during integrated circuit fabrication. Promptly detecting abnormal wafers is an important way to increase yield and product quality. However, research on identifying spatial defect patterns has focused only on flash memory with a single wafer map. No procedure is available for identifying spatial defect patterns on dynamic random access memory (DRAM) with multiple wafer maps. This paper proposes a new step-down spatial randomness test for detecting abnormalities on a DRAM wafer with multiple spatial maps. We adopt nonparametric Gaussian kernel-density estimation to transform the original fail bit test (FBT) values into binary FBT values. We also propose a spatial local de-noising method to eliminate noisy defect chips to distinguish the random defect patterns from systematic ones. We experimentally validated the proposed procedure using real-life DRAM wafers. These experimental results demonstrate that our approach can viably replace manual detection of abnormal DRAM wafers.
dc.language
eng
dc.relation.ispartofseries
IEEE Transactions on Semiconductor Manufacturing
dc.title
Step-Down Spatial Randomness Test for Detecting Abnormalities in DRAM Wafers with Multiple Spatial Maps
dc.citation.endPage
65
dc.citation.number
1
dc.citation.startPage
57
dc.citation.volume
29
dc.subject.keyword
Data mining
dc.subject.keyword
Big data
dc.subject.keyword
Anomaly detection
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7. KISTI 연구성과 > 학술지 발표논문
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