Predicting the critical features of the chemically-amplified resist profile based on machine learning

Pengjie Kong; Lisong Dong; Xu Ma; Yayi Wei

doi:10.1117/12.2658664

30 April 2023 Predicting the critical features of the chemically-amplified resist profile based on machine learning

Pengjie Kong, Lisong Dong, Xu Ma, Yayi Wei

Proceedings Volume 12498, Advances in Patterning Materials and Processes XL; 124981U (2023) https://doi.org/10.1117/12.2658664
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

The improvement of accuracy and efficiency in simulating the profile of the chemically amplified resist (CAR) is always a key point in lithography. With the development of machine learning, many models have been successfully applied in optical proximity correction (OPC), hotspot detection, and other lithographic fields. In this work, we developed a neural network for predicting the critical features’ sizes of the CAR profile. By using a pre-calibrated physical resist model, the effectiveness of this model is demonstrated from numerical simulation. The results indicate that for the critical dimensions (CDs) of the CAR profile, this model shows great speed and accuracy. After applying the tuned neural network on the test sets, it shows 92.98% of the test sets have a mean square error (MSE) less than 1%.

Citation Download Citation

Pengjie Kong, Lisong Dong, Xu Ma, and Yayi Wei "Predicting the critical features of the chemically-amplified resist profile based on machine learning", Proc. SPIE 12498, Advances in Patterning Materials and Processes XL, 124981U (30 April 2023); https://doi.org/10.1117/12.2658664

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