Model-based placement and optimization of subresolution assist features

Levi D. Barnes; Benjamin D. Painter; Lawrence S. Melvin III

doi:10.1117/12.656691

15 March 2006 Model-based placement and optimization of subresolution assist features

Levi D. Barnes, Benjamin D. Painter, Lawrence S. Melvin III

Proceedings Volume 6154, Optical Microlithography XIX; 61542C (2006) https://doi.org/10.1117/12.656691
Event: SPIE 31st International Symposium on Advanced Lithography, 2006, San Jose, California, United States

Abstract

Sub-resolution assist features (SRAFs) are an important tool for improving through-process robustness of advanced lithographic processes. Assist features have generally been placed and adjusted according to heuristic rules. The complexity of these rules increases rapidly with shrinking features size requiring more wafer data for calibration and more effort on the part of engineers. For advanced nodes, a model-based approach may better account for the variety of two-dimensional geometries and reduce substantially the amount of user effort required for effective SRAF placement. There are many ways in which model-based methods can be used to improve the effectiveness of assist features; we investigate several here. In the investigations described here, process window models may be employed to: 1) derive optimal rules for initial AF placement in a rule-based process, 2) resolve mask rule violations in optimal ways, and 3) make post-placement corrections of mask sites with poor behavior. In addition, we discuss a method for replacing an initial rule-based assist feature placement with a model-based placement which can consider the local two-dimensional geometry.

Citation Download Citation

Levi D. Barnes, Benjamin D. Painter, and Lawrence S. Melvin III "Model-based placement and optimization of subresolution assist features", Proc. SPIE 6154, Optical Microlithography XIX, 61542C (15 March 2006); https://doi.org/10.1117/12.656691

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