A stochastic approach to SRAF printing prediction

Andrey Lutich

doi:10.1117/12.2045897

31 March 2014 A stochastic approach to SRAF printing prediction

Andrey Lutich

Author Affiliations +

Proceedings Volume 9052, Optical Microlithography XXVII; 905225 (2014) https://doi.org/10.1117/12.2045897
Event: SPIE Advanced Lithography, 2014, San Jose, California, United States

Abstract

In this paper we discuss a statistic approach to SRAF printing detection. This method considers and is generically based on the opportunistic essence of the resist image formation due to various stochastic uncertainties in the state-of-the-art advanced node lithographic process. The method is based on the direct measurement of the probability of SRAF printing at considered-to-be-nominal conditions and extrapolation of the results based on the process conditions/assumptions to the probability ranges where direct probability measurements are practically impossible. The method described here provides a controllable and quantitative framework for setting up SRAF printing detection and facilitates the significant reduction of the efforts and costs needed to setup SRAF printing checks. The argumentation and the way of looking at the verification setup in the environments with considerable variability can be directly reused to design and calibrate other checks in OPC verification flows.

Citation Download Citation

Andrey Lutich "A stochastic approach to SRAF printing prediction", Proc. SPIE 9052, Optical Microlithography XXVII, 905225 (31 March 2014); https://doi.org/10.1117/12.2045897

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