Correlation of scatterometry sensitivities to variation in device parameters

Chun-Hung Ko; Yi-sha Ku; Nigel Smith

doi:10.1117/12.656147

24 March 2006 Correlation of scatterometry sensitivities to variation in device parameters

Chun-Hung Ko, Yi-sha Ku, Nigel Smith

Proceedings Volume 6152, Metrology, Inspection, and Process Control for Microlithography XX; 615220 (2006) https://doi.org/10.1117/12.656147
Event: SPIE 31st International Symposium on Advanced Lithography, 2006, San Jose, California, United States

Abstract

Scatterometry has been demonstrated to be a useful measurement technique, which allows to examine a full reconstruction of the measured structure in the semiconductor process, e.g. CD, thickness, and overlay. Even though the potential of such technique has been known for many years, the challenge for extracting quickly and accurately the relevant constitutive parameters from a diffractive signature remains. In general, the device parameters are determined by finding the minimum RMSE (root mean square error) between a measured signature and theoretical signatures in the model-based library without considering the correlation among these parameters, which induces the match error problem. This study presents a novel method, applying neural network algorithm to identify the correlation between device parameters, to reduce the correlation-induced error and increase measurement precision.

Citation Download Citation

Chun-Hung Ko, Yi-sha Ku, and Nigel Smith "Correlation of scatterometry sensitivities to variation in device parameters", Proc. SPIE 6152, Metrology, Inspection, and Process Control for Microlithography XX, 615220 (24 March 2006); https://doi.org/10.1117/12.656147

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available