Process model quality assessment by sensitivity analysis

Qiliang Yan; Lawrence S. Melvin III; James P. Shiely

doi:10.1117/12.617197

28 June 2005 Process model quality assessment by sensitivity analysis

Qiliang Yan, Lawrence S. Melvin III, James P. Shiely

Proceedings Volume 5853, Photomask and Next-Generation Lithography Mask Technology XII; (2005) https://doi.org/10.1117/12.617197
Event: Photomask and Next Generation Lithography Mask Technology XII, 2005, Yokohama, Japan

Abstract

An accurate process model is the linchpin of model-based Optical Proximity Correction (OPC) and Resolution Enhancement Technique (RET) synthesis. The accuracy of the resulting mask layout can be no better than that of the model. Relatively good, first-principle mathematical models exist for some process steps, such as aerial image formation, but resulting silicon is a combination of many effects, including those less well understood. Accuracy can be assured only with models anchored to observed phenomena. Process models are usually a combination of first principle elements and phenomenological components with the “right” degrees to freedom to fit the overall process. The key challenge in generating accurate models is to capture all process behavior over all conditions with a minimum number of empirical measurements. This means that models must extrapolate accurately from the specifics measured, and should be largely immune to empirical measurement noise. In this paper we describe a methodology in which to test model performance with respect to these criteria.

Citation Download Citation

Qiliang Yan, Lawrence S. Melvin III, and James P. Shiely "Process model quality assessment by sensitivity analysis", Proc. SPIE 5853, Photomask and Next-Generation Lithography Mask Technology XII, (28 June 2005); https://doi.org/10.1117/12.617197

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