By Topic

Quantifying the value of ownership of yield analysis technologies

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

4 Author(s)
C. Weber ; Sloan Sch. of Manage., MIT, Cambridge, MA, USA ; V. Sankaran ; G. Scher ; K. W. Tobin

A model based on information theory, which allows yield managers to determine the optimal portfolio of yield analysis technologies for both the R&D and volume production environments, is presented. The knowledge extraction per experimentation cycle and knowledge extraction per unit time serve as benchmarking metrics for yield learning. They enable yield managers to make objective comparisons of apparently unrelated technologies. Combinations of four yield analysis tools-electrical testing, automatic defect classification, spatial signature analysis and wafer position analysis-are examined in detail to determine the relative value of ownership of different yield analysis technologies

Published in:

Advanced Semiconductor Manufacturing Conference and Workshop, 1999 IEEE/SEMI

Date of Conference: