Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm

Tae San Kim, Jong Wook Lee, Won Kyung Lee () and So Young Sohn ()
Additional contact information
Tae San Kim: Yonsei University
Jong Wook Lee: Yonsei University
Won Kyung Lee: Yonsei University
So Young Sohn: Yonsei University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 6, No 9, 1715-1724

Abstract: Abstract In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process. The rapid advancement of the integrated circuit technology has recently led to more frequent occurrences of mixed-type defect patterns, wherein two or more defect patterns simultaneously occur in a wafer bin map. The detection of these mixed patterns is more difficult than that of single patterns. To detect these mixed patterns, binary relevance approaches based on convolutional neural networks have been proposed. However, as the manufacturing process has been advanced and integrated, various failure types are newly detected, thus the number of single models can be continuously increased following the diversification of defect types. Therefore, we propose an effective framework for detecting mixed-type patterns in which a simple single model, called the single shot detector, is employed. By applying the proposed model to the WM-811K dataset, we show that our framework outperforms existing CNN-based methods and also provides defect location information.

Keywords: Object detection; Single shot detection; Defect detection; Mixed pattern detection (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01755-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01755-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-021-01755-6

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().