10 0

A generalised uncertain decision tree for defect classification of multiple wafer maps

Title
A generalised uncertain decision tree for defect classification of multiple wafer maps
Author
김병훈
Keywords
DRAM; semiconductor wafer; uncertain data classification; uncertain feature
Issue Date
2019-07
Publisher
TAYLOR & FRANCIS LTD
Citation
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, Page. 1-17
Abstract
Classification of defect chip patterns is one of the most important tasks in semiconductor manufacturing process. During the final stage of the process just before release, engineers must manually classify and summarise information of defect chips from a number of wafers that can aid in diagnosing the root causes of failures. Traditionally, several learning algorithms have been developed to classify defect patterns on wafer maps. However, most of them focused on a single wafer bin map based on certain features. The objective of this study is to propose a novel approach to classify defect patterns on multiple wafer maps based on uncertain features. To classify distinct defect patterns described by uncertain features on multiple wafer maps, we propose a generalised uncertain decision tree model considering correlations between uncertain features. In addition, we propose an approach to extract uncertain features of multiple wafer maps from the critical fail bit test (FBT) map, defect shape, and location based on a spatial autocorrelation method. Experiments were conducted using real-life DRAM wafers provided by the semiconductor industry. Results show that the proposed approach is much better than any existing methods reported in the literature.
URI
https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1637035http://repository.hanyang.ac.kr/handle/20.500.11754/117394
ISSN
0020-7543; 1366-588X
DOI
10.1080/00207543.2019.1637035
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE