Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Sample-size determination for achieving asymptotic stability of a double EWMA control scheme

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)
Sheng-Tsaing Tseng ; Nat. Tsing Hua Univ., Hsinchu, Taiwan ; Nan-Jung Hsu

The double exponentially weighted moving average (dEWMA) feedback control scheme, a conventional run-to-run control scheme, can adjust certain semiconductor manufacturing processes with a linear drift. The long-term stability conditions for this closed-loop system have received considerable attention in literature. These stability conditions can be expressed in terms of the predicted model assuming that an initial process input-output (I-O) predicted model can be obtained successfully in advance. However, the predicted model is constructed by a random sample of I-O variables, and therefore the strength of the linear relationship between I-O variables plays a major role in determining the validation of these stability conditions. In order to design a stable dEWMA control scheme, the covariance (or correlation) structure of I-O variables and the number of experiments should be simultaneously considered. By controlling a guaranteed probability of stability, this study first derives the formula for an adequate sample size required to construct the predicted model in the case of single-input single-output and multiple-input single-output systems. Illustrative examples demonstrate the effectiveness of the covariance structure of I-O variables in determining the sample size. Implications for research on multiple-input multiple-output systems are also addressed.

Published in:

Semiconductor Manufacturing, IEEE Transactions on  (Volume:18 ,  Issue: 1 )