Evaluation of deep learning model for 3D profiling of HAR features using high-voltage CD-SEM

Wei Sun; Yasunori Goto; Takuma Yamamoto; Keiichiro Hitomi

doi:10.1117/12.2592052

22 February 2021 Evaluation of deep learning model for 3D profiling of HAR features using high-voltage CD-SEM

Wei Sun, Yasunori Goto, Takuma Yamamoto, Keiichiro Hitomi

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116110X (2021) https://doi.org/10.1117/12.2592052
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

3D-NAND memory will continue to increase in the aspect ratio of channel holes. High throughput and in-line monitoring solutions for 3D profiling of high aspect ratio (HAR) features are the key for yield improvement. A deep learning (DL) model has been developed to improve the 3D profiling accuracy of the HAR features. In this work, the HAR holes with different bowing geometries were fabricated and a high-voltage CD-SEM was used to evaluate the performance of the DL model. The accuracy and the sensitivity of the DL model was evaluated by comparing the predicted cross-sections with the X-SEM measurement. The results show that the DL model enables the maximum CD (MCD) of the bowing features to be predicted with a sensitivity of 0.93 and its depth position to be predicted with a sensitivity of 0.91. The DL learning model reduced the absolute error of the predicted MCD depth position from several hundreds of nanometers, the error occurring when using the exponential model, to within 100 nm.

Conference Presentation

Citation Download Citation

Wei Sun, Yasunori Goto, Takuma Yamamoto, and Keiichiro Hitomi "Evaluation of deep learning model for 3D profiling of HAR features using high-voltage CD-SEM", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116110X (22 February 2021); https://doi.org/10.1117/12.2592052

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