By Topic

Spatial Variance Spectrum Analysis and Its Application to Unsupervised Detection of Systematic Wafer Spatial Variations

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

2 Author(s)
Jakey Blue ; Department of Mechanical Engineering, National Taiwan University, Taipei, ROC (Taiwan) ; Argon Chen

Investigation of wafer spatial variations is critical for semiconductor process/equipment optimization and circuit design. The objective of spatial variation study is to differentiate the systematic variation component from the random component. This is usually done by contrasting with a set of known systematic patterns based on engineering knowledge. However, there could exist unknown systematic components remaining in the unexplained residuals and overlooked by the conventional spatial variation study. In this paper, we develop a novel spatial variance spectrum (SVS) to analyze the systematic variations without any priori information of the systematic patterns. The SVS is a series of spatial variations over a range of spatial moving window sizes from the smallest spatial moving window consisting of only two metrology sites to the largest one covering all metrology sites of the entire wafer. The SVS can be used to characterize the wafer spatial variations and to detect existence of systematic variations by a proposed hypothesis test. We also propose an index to summarize from the SVS the systematic proportion of the spatial variation. The proposed test and index of systematic variations will be demonstrated and validated through both hypothetical examples and actual cases of wafer critical dimension (CD) metrology data.

Published in:

IEEE Transactions on Automation Science and Engineering  (Volume:8 ,  Issue: 1 )