Share Email Print

Proceedings Paper

A predictive method to forecast spatial variability of stochastic processes for deep nanoscale semiconductor manufacturing
Author(s): Yijian Chen
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A general predictive method based on Canonical Correlation Analysis (CCA) is developed to identify globally correlated process modes that are responsible for the spatial variability in deep nanoscale semiconductor manufacturing. This multivariate statistical method overcomes the limitations of ordinary multiple linear regression technique by introducing canonical variates with certain properties which allow us to construct a transfer matrix to relate the predictand vector to the predictor vector directly. Principal Component Analysis (PCA), another multivariate statistical technique, is introduced to find the orthogonal modes that explain the larger fraction of the total process variations. We also discuss the constraint of sample number in CCA and propose using the leading principal components (PCAs) to replace the original raw data in correlation analysis.

Paper Details

Date Published: 5 April 2007
PDF: 11 pages
Proc. SPIE 6518, Metrology, Inspection, and Process Control for Microlithography XXI, 65182O (5 April 2007); doi: 10.1117/12.710986
Show Author Affiliations
Yijian Chen, VIGMA Nanoelectronics (United States)

Published in SPIE Proceedings Vol. 6518:
Metrology, Inspection, and Process Control for Microlithography XXI
Chas N. Archie, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?