Deep supervised learning to estimate true rough line images from SEM images Conference Paper uri icon

abstract

  • 2018 SPIE. 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.

published proceedings

  • Proceedings of SPIE - The International Society for Optical Engineering

altmetric score

  • 3

author list (cited authors)

  • Chaudhary, N., Savari, S. A., & Yeddulapalli, S. S.

citation count

  • 6

complete list of authors

  • Chaudhary, Narendra||Savari, Serap A||Yeddulapalli, Sai Swaroop

editor list (cited editors)

  • Behringer, U. F., & Finders, J. o.

publication date

  • January 2018