Incorporating photomask shape uncertainty in computational lithography

Xiaofei Wu; Shiyuan Liu; Andreas Erdmann; Edmund Y. Lam

doi:10.1117/12.2220278

15 March 2016 Incorporating photomask shape uncertainty in computational lithography

Xiaofei Wu, Shiyuan Liu, Andreas Erdmann, Edmund Y. Lam

Proceedings Volume 9780, Optical Microlithography XXIX; 97800Q (2016) https://doi.org/10.1117/12.2220278
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

Abstract

The lithographic performance of a photomask is sensitive to shape uncertainty caused by manufacturing and measurement errors. This work proposes incorporating the photomask shape uncertainty in computational lithography such as inverse lithography. The shape uncertainty of the photomask is quantitatively modeled as a random ﬁeld in a level-set method framework. With this, the shape uncertainty can be characterized by several parameters, making it computationally tractable to be incorporated in inverse lithography technique (ILT). Simulations are conducted to show the eﬀectiveness of using this method to represent various kinds of shape variations. It is also demonstrated that incorporating the shape variation in ILT can reduce the mask error enhancement factor (MEEF) values of the optimized patterns, and improve the robustness of imaging performance against mask shape ﬂuctuation.

Citation Download Citation

Xiaofei Wu, Shiyuan Liu, Andreas Erdmann, and Edmund Y. Lam "Incorporating photomask shape uncertainty in computational lithography", Proc. SPIE 9780, Optical Microlithography XXIX, 97800Q (15 March 2016); https://doi.org/10.1117/12.2220278

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