Deep supervised learning to estimate true rough line images from SEM images

Narendra Chaudhary; Serap A. Savari; S. S. Yeddulapalli

doi:10.1117/12.2324341

19 September 2018 Deep supervised learning to estimate true rough line images from SEM images

Narendra Chaudhary, Serap A. Savari, S. S. Yeddulapalli

Proceedings Volume 10775, 34th European Mask and Lithography Conference; 107750R (2018) https://doi.org/10.1117/12.2324341
Event: 34th European Mask and Lithography Conference, 2018, Grenoble, France

Abstract

We use deep supervised learning for the Poisson denoising of low-dose scanning electron microscope (SEM) images as a step in the estimation of line edge roughness (LER) and line width roughness (LWR). Our denoising algorithm applies a deep convolutional neural network called SEMNet with 17 convolutional, 16 batch-normalization and 16 dropout layers to noisy images. We trained and tested SEMNet with a dataset of 100800 simulated SEM rough line images constructed by means of the Thorsos method and the ARTIMAGEN library developed by the National Institute of Standards and Technology. SEMNet achieved considerable improvements in peak signal-to-noise ratio (PSNR) as well as the best LER/LWR estimation accuracy compared with standard image denoisers.

Citation Download Citation

Narendra Chaudhary, Serap A. Savari, and S. S. Yeddulapalli "Deep supervised learning to estimate true rough line images from SEM images", Proc. SPIE 10775, 34th European Mask and Lithography Conference, 107750R (19 September 2018); https://doi.org/10.1117/12.2324341

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