Improvement of EPE measurement accuracy on ADI wafer, the method of using machine learning trained with CAD

Yosuke Okamoto; Shinichi Nakazawa; Akinori Kawamura; Taihei Mori; Kotaro Maruyama; Seul-Ki Kang; Yuichiro Yamazaki

doi:10.1117/12.2584709

22 February 2021 Improvement of EPE measurement accuracy on ADI wafer, the method of using machine learning trained with CAD

Yosuke Okamoto, Shinichi Nakazawa, Akinori Kawamura, Taihei Mori, Kotaro Maruyama, Seul-Ki Kang, Yuichiro Yamazaki

Author Affiliations +

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116111W (2021) https://doi.org/10.1117/12.2584709
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

The precise metrology for edge placement error (EPE) is required especially in EUV era. Last year, we proposed new contour extraction algorithm using machine learning and verified the robustness to SEM noise on AEI pattern. In this study, we suggest the method for contour extraction on ADI pattern and improve the EPE measurement accuracy. It is known that the gray-level signal profile across the pattern edge on SEM image is varied depending on e-beam scan direction angle to the pattern edge, and especially the contrast of parallel pattern edge to scan direction is low and unstable. In addition, in case of ADI, the gray-level of SEM image are varied and have the shading because of charge effect caused by e-beam exposure on the pattern. Therefore, the contour extraction on ADI pattern just using simple feature value or some of thresholds is usually inaccurate. However, the precise contour extraction independent on e-beam scan direction is required strongly for 2D pattern inspection and metrology. In this paper, we will propose the novel contour extraction method of precise EPE metrology on ADI regardless of the ebeam scanning direction to the pattern edge. We use machine learning to extract contour, splitted training data according to target edge direction, and trained contour extraction model. This model is expected to learn not only the gray-level variation but also the drift of landing position caused by the charge effect on ADI. We captured SEM images on the ADI wafer with several scan direction and compared between the contours extracted by the conventional method and extracted by the proposal method, then the improvement of EPE measurement accuracy at every pattern direction on ADI is verified.

Conference Presentation

Citation Download Citation

Yosuke Okamoto, Shinichi Nakazawa, Akinori Kawamura, Taihei Mori, Kotaro Maruyama, Seul-Ki Kang, and Yuichiro Yamazaki "Improvement of EPE measurement accuracy on ADI wafer, the method of using machine learning trained with CAD", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116111W (22 February 2021); https://doi.org/10.1117/12.2584709

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