By Topic

A Unified Framework for Outlier Detection in Trace Data Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Zhiguo Li ; IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA ; Baseman, R.J. ; Zhu, Y. ; Tipu, F.A.
more authors

Process trace data (PTD) is an important data type in semiconductor manufacturing and has a very large aggregate volume. While data mining and statistical analysis play a key role in the quality control of wafers, the existence of outliers adversely affects the applications benefiting from PTD analysis. Due to the complexities of PTD and the resultant outlier patterns, this paper proposes a unified outlier detection framework which takes advantages of data complexity reduction using entropy and abrupt change detection using cumulative sum (CUSUM) method. To meet the practical needs of PTD analysis, a two-step algorithm taking into account of the related domain knowledge is developed, and its effectiveness is validated by using real PTD sets and a production example. The experimental results show that the proposed method outperforms the Fast Greedy Algorithm (FGA) and the Grubb's test, two commonly used outlier detection techniques for univariate data.

Published in:

Semiconductor Manufacturing, IEEE Transactions on  (Volume:27 ,  Issue: 1 )