Application of artificial neural networks (ANN) and response surface model (RSM) in optical microlithographic process modeling

Bo Zhou; Barry A. McPherron

doi:10.1117/12.310816

29 June 1998 Application of artificial neural networks (ANN) and response surface model (RSM) in optical microlithographic process modeling

Bo Zhou, Barry A. McPherron

Author Affiliations +

Proceedings Volume 3334, Optical Microlithography XI; (1998) https://doi.org/10.1117/12.310816
Event: 23rd Annual International Symposium on Microlithography, 1998, Santa Clara, CA, United States

Abstract

Optical microlithography represents one of the most sophisticated processes in the manufacturing of microelectronics devices. Accurate process models are highly desirable for process control, process optimization, yield improvement, and cost reduction. Design of experiments (DOE) and response surface model (RSM) are traditional tools for empirical modeling. This paper presents an alternative by using artificial neural networks (ANNs) to model the intricate relationship between the critical dimension (CD) and three key lithographic process variables, soft bake time, exposure stage speed, and develop time. A set of data obtained from a designed experiment is used to train a three-layer neural network. A comparison of the ANN model with the RSM model shows that ANN model provides higher accuracy and greater capability of generalization.

Citation Download Citation

Bo Zhou and Barry A. McPherron "Application of artificial neural networks (ANN) and response surface model (RSM) in optical microlithographic process modeling", Proc. SPIE 3334, Optical Microlithography XI, (29 June 1998); https://doi.org/10.1117/12.310816

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