Deep supervised learning to estimate true rough line images from SEM images
| Autor: | Narendra Chaudhary, Sai Swaroop Yeddulapalli, Serap A. Savari |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Noise reduction Deep learning Supervised learning Pattern recognition 02 engineering and technology Surface finish Inverse problem 01 natural sciences Convolutional neural network 010309 optics Computer Science::Computer Vision and Pattern Recognition 0103 physical sciences Line (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Dropout (neural networks) |
| Zdroj: | 34th European Mask and Lithography Conference. |
| DOI: | 10.1117/12.2324341 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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