Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis

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

2 Author(s)
Cherry, G.A. ; Adv. Micro Devices Inc., Austin, TX, USA ; Qin, S.J.

The purposes of multivariate statistical process control (MSPC) are to improve process operations by quickly detecting when process abnormalities have occurred and diagnosing the sources of the process abnormalities. In the area of semiconductor manufacturing, increased yield and improved product quality result from reducing the amount of wafers produced under suboptimal operating conditions. This paper presents a complete MSPC application method that combines recent contributions to the field, including multiway principal component analysis (PCA), recursive PCA, fault detection using a combined index, and fault contributions from Hotelling's T2 statistic. In addition, a method for determining multiblock fault contributions to the combined index is introduced. The effectiveness of the system is demonstrated using postlithography metrology data and plasma stripper processing tool data.

Published in:

Semiconductor Manufacturing, IEEE Transactions on  (Volume:19 ,  Issue: 2 )