Resist-slope aware modeling for mask process correction applications

Rachit Sharma; Ingo Bork; Kushlendra Mishra; ShiZhi Lyu; Linna Cong; Mingjing Tian; Malavika Sharma; Bhardwaj Durvasula; Nageswara Rao; Peter Buck

doi:10.1117/12.2536529

26 September 2019 Resist-slope aware modeling for mask process correction applications

Rachit Sharma, Ingo Bork, Kushlendra Mishra, ShiZhi Lyu, Linna Cong, Mingjing Tian, Malavika Sharma, Bhardwaj Durvasula, Nageswara Rao, Peter Buck

Author Affiliations +

Proceedings Volume 11148, Photomask Technology 2019; 111480G (2019) https://doi.org/10.1117/12.2536529
Event: SPIE Photomask Technology + EUV Lithography, 2019, Monterey, California, United States

Abstract

This work presents our investigations on a new resist-slope kernel for Mask Process Correction (MPC) applications, specifically modeling the contribution (including linear and higher-order) of the resist image slope to the overall etch bias. Mask Process Correction (MPC) models with different complexities, i.e., varying number of kernels, were calibrated and compared against each other for model accuracy, layout correction run-time and dose-dependent residual trends. The results demonstrate that using the resist-slope kernel with a simpler model can allow for up to 40 percent lower correction run-time (compared to complex models) without a major degradation of the overall model accuracy. Hence, this paper presents the resist-slope kernel as a valuable addition to MPC modeling techniques, especially for situations where conventional methods are not sufficient to meet the accuracy or run time requirements.

Citation Download Citation

Rachit Sharma, Ingo Bork, Kushlendra Mishra, ShiZhi Lyu, Linna Cong, Mingjing Tian, Malavika Sharma, Bhardwaj Durvasula, Nageswara Rao, and Peter Buck "Resist-slope aware modeling for mask process correction applications", Proc. SPIE 11148, Photomask Technology 2019, 111480G (26 September 2019); https://doi.org/10.1117/12.2536529

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