Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning

Stefan Hunsche; Qian Zhao; Xu Xie; Robert Socha; Hua-Yu Liu; Peter Nikolsky; Anthony Ngai; Paul van Adrichem; Michael Crouse; Ivan Lalovic

doi:10.1117/12.814644

6 March 2009 Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning

Stefan Hunsche, Qian Zhao, Xu Xie, Robert Socha, Hua-Yu Liu, Peter Nikolsky, Anthony Ngai, Paul van Adrichem, Michael Crouse, Ivan Lalovic

Author Affiliations +

Proceedings Volume 7274, Optical Microlithography XXII; 727405 (2009) https://doi.org/10.1117/12.814644
Event: SPIE Advanced Lithography, 2009, San Jose, California, United States

Abstract

Computational lithography (CL) is becoming more and more of a fundamental enabler of advanced semiconductor processing technology, and new requirements for CL models are arising from new applications such as model-based process tuning. In this paper we study the impact of realistic machine parameters that can be incorporated in a modern CL model, and provide an experimental assessment of model improvements with respect to prediction of scanner tuning effects. The data demonstrates improved model accuracy and prediction by inclusion of scanner-type specific modeling capabilities and machine data in the CL model building process. In addition to scanner effects, we study laser bandwidth tuning effects and the accuracy of corresponding model predictions by comparison against experimental data. The data demonstrate that the models predict well wafer CD variations resulting from laser BW tuning. We also find that using realistic spectral density distribution of the laser can provide more accurate results than the commonly assumed modified Lorentzian line shape.

Citation Download Citation

Stefan Hunsche, Qian Zhao, Xu Xie, Robert Socha, Hua-Yu Liu, Peter Nikolsky, Anthony Ngai, Paul van Adrichem, Michael Crouse, and Ivan Lalovic "Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning", Proc. SPIE 7274, Optical Microlithography XXII, 727405 (6 March 2009); https://doi.org/10.1117/12.814644

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