A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Bappaditya Dey; Stewart Wu; Victor Blanco; Samir Bhamidipati; Amr Essam; Kevin Ahi; Sandip Halder; Germain Fenger

doi:10.1117/12.2680884

5 October 2023 A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Bappaditya Dey, Stewart Wu, Victor Blanco, Samir Bhamidipati, Amr Essam, Kevin Ahi, Sandip Halder, Germain Fenger

Author Affiliations +

Proceedings Volume 12802, 38th European Mask and Lithography Conference (EMLC 2023); 1280208 (2023) https://doi.org/10.1117/12.2680884
Event: 38th European Mask and Lithography Conference, 2023, Dresden, Germany

Abstract

Flow from circuit design to manufacturing a mask is a complex process. A mask is used to print a particular design feature. Extraction of accurate CD contour geometries from ADI (after developed inspection) SEM images plays a pivotal role for a qualitative lithographic process as well as to verify device characterization in aggressive pitches. In our previous work, it has been shown how deep learning based de-noising is helping to improve the contour detection accuracy. We analysed and validated our result with noisy/denoised image pair for categorically different geometrical patterns, such as, L/S (linespace), T2T (tip-to-tip), pillars with different scan types etc., by using a programmable tool (Calibre®SEMSuite^TM) for contour extraction and further analysis metrics. The comparative analysis demonstrated that denoised images have significantly higher confidence analysis metrics, reduced number of missing patterns as well as false bridges against raw noisy images while keeping the same parameter settings for both data inputs. We have demonstrated that our proposed method is capable to extract contours on the body of the noisy SEM images with accuracy in close proximity with design data. By combining these advanced algorithms as options in Calibre®SEMSuite^TM, users would be able to process large amount of information for data cleaning, classification & further model calibration intelligently.

Citation Download Citation

Bappaditya Dey, Stewart Wu, Victor Blanco, Samir Bhamidipati, Amr Essam, Kevin Ahi, Sandip Halder, and Germain Fenger "A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures", Proc. SPIE 12802, 38th European Mask and Lithography Conference (EMLC 2023), 1280208 (5 October 2023); https://doi.org/10.1117/12.2680884

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
17 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Contour extraction

Scanning electron microscopy

Image quality

Deep learning

Denoising

Image processing

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years