By Topic

Supervised learning methods in sort yield modeling

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
$33 $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

1 Author(s)
Helen Hu ; GLOBALFOUNDRIES, 1050 E Arques Ave, MS143, Sunnyvale, CA 94085, USA

Supervised learning consists of a large variety of methods that explore data relationships. The techniques described in this paper cover those methods that are robust and relevant to semiconductor data, sufficiently simple for use by non-statisticians, and proven effective in yield modeling. We first apply the classification and regression tree (CART) technique to detect the source of yield variations from electrical parameters and process equipment. Yield prediction models, including multinomial logistic regression (MNL) and the random forest (RF) method, will also be discussed. Case studies demonstrate the strength of combining traditional regression with machine learning techniques.

Published in:

2009 IEEE/SEMI Advanced Semiconductor Manufacturing Conference

Date of Conference:

10-12 May 2009