Chemically amplified resist modeling in OPC

Xin Zheng; Jason Huang; Fred Kuo; Aram Kazarian; Fook Chin; Yongfa Fan

doi:10.1117/12.814354

16 March 2009 Chemically amplified resist modeling in OPC

Xin Zheng, Jason Huang, Fred Kuo, Aram Kazarian, Fook Chin, Yongfa Fan

Author Affiliations +

Proceedings Volume 7274, Optical Microlithography XXII; 727435 (2009) https://doi.org/10.1117/12.814354
Event: SPIE Advanced Lithography, 2009, San Jose, California, United States

Abstract

The mechanism of chemically amplified resist plays a critical role in the modeling of the latent image. To achieve a practical model which can fit into the time frame of OPC, some simplifications and assumptions have to be made. We introduced regression kernels that take into account best exposure focus difference between isotropic pitch, dense, and line end features for the evaluation of image intensity. It compares the image intensity (signal) over small changes above and/or below the regressed "nominal" image position, which in principle corresponds to evaluating the intensity signal at various depths of a fixed resist profile thus can also be regressed for optimization during model development. Our calibration has shown that the model brought a great improvement in prediction for difficult structures such as dense features at or near the optical resolution limit and 2-dimensional features, which are the limiter of the overall model fitting accuracy for 45nm node and below. By replacing other existing techniques, total number of output kernels used for OPC operation is actually reduced with improvement of model accuracy. This model is proven to be a very effective yet accurate addition to the current OPC technology.

Citation Download Citation

Xin Zheng, Jason Huang, Fred Kuo, Aram Kazarian, Fook Chin, and Yongfa Fan "Chemically amplified resist modeling in OPC", Proc. SPIE 7274, Optical Microlithography XXII, 727435 (16 March 2009); https://doi.org/10.1117/12.814354

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