Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wa...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on semiconductor manufacturing Vol. 28; no. 1; pp. 1 - 12
Main Authors:	Ming-Ju Wu, Jang, Jyh-Shing R., Jui-Long Chen
Format:	Journal Article
Language:	English
Published:	IEEE 01-02-2015
Subjects:	data models Fabrication Feature extraction image recognition information retrieval Pattern recognition semiconductor defects Semiconductor device modeling Support vector machines Transforms Data models semiconductor defects image recognition information retrieval pattern recognition
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world's largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.
ISSN:	0894-6507 1558-2345
DOI:	10.1109/TSM.2014.2364237