Machine learning (ML) based SEM contour extraction accelerated by GPU for etch modeling application

Yuansheng Ma; Xuefeng Zeng; Yunxiang Wang; Haizhou Yin; Xiaoyuan Qi; Shibing Wang; Le Hong

doi:10.1117/12.3012442

10 April 2024 Machine learning (ML) based SEM contour extraction accelerated by GPU for etch modeling application

Yuansheng Ma, Xuefeng Zeng, Yunxiang Wang, Haizhou Yin, Xiaoyuan Qi, Shibing Wang, Le Hong

Proceedings Volume PC12958, Advanced Etch Technology and Process Integration for Nanopatterning XIII; (2024) https://doi.org/10.1117/12.3012442
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

In this paper, we introduce a method that employs a deep learning model, built with GPU, to extract contours from a variety of SEM images. The model is trained with images and their corresponding ground truth. Various models are explored, and their predictive results are juxtaposed with the known ground truth. In comparison with CPU, utilizing GPU can augment the speed approximately 20 times.

Conference Presentation

Citation Download Citation

Yuansheng Ma, Xuefeng Zeng, Yunxiang Wang, Haizhou Yin, Xiaoyuan Qi, Shibing Wang, and Le Hong "Machine learning (ML) based SEM contour extraction accelerated by GPU for etch modeling application", Proc. SPIE PC12958, Advanced Etch Technology and Process Integration for Nanopatterning XIII, (10 April 2024); https://doi.org/10.1117/12.3012442

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