Deep learning denoising of SEM images towards noise-reduced LER measurements

Giannatou, Eva; Papavieros, George; Constantoudis, Vassilios; Papageorgiou, Harris; Gogolides, Εvangelos

doi:10.1016/j.mee.2019.111051

Cited by 39 publications

(34 citation statements)

References 7 publications

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“…The reference metrology for measuring the 3D sidewall's shape can be used for evaluating SEM and other dimensional-measurement techniques such as tilt-beam (or 3D-) SEM and normal AFM, and will be useful for providing reference input data for deep learning-aided LER metrology. 24,25…”

Section: Discussion On Ler Reference Metrologymentioning

confidence: 99%

Direct comparison of line edge roughness measurements by SEM and a metrological tilting-atomic force microscopy for reference metrology

Kizu

Misumi

Hirai

et al. 2020

J. Micro/Nanolith. MEMS MOEMS

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Background: Conventional scanning electron microscopy (SEM) that is used for 2D top-view metrology, a classical line edge roughness (LER) measurement technique, is incapable of measuring 3D structures of a nanoscale line pattern. For LER measurements, SEM measurement generates a single line-edge profile for the 3D sidewall roughness, although the line-edge profile differs at each height in the 3D sidewall. Aim: To develop an evaluation method of SEM-based LER measurement techniques and to verify how the 3D sidewall shape is reflected in the SEM's 2D result. Approach: Direct comparison by measuring an identical location of a line pattern by SEM and an atomic force microscopy (AFM) with the tip-tilting technique that is capable of measuring the 3D sidewall. The line pattern has vertical stripes on the sidewall due to its fabrication process. Measured line edge profiles were analyzed using power spectral density, height-height correlation function, and autocorrelation function. Results: Line edge profiles measured by SEM and AFM were well matched except for noise level. Frequency and scaling analyses showed that SEM profile contained high noise and had lost a property of self-affine fractals in contrast to AFM. Conclusions: In the case of the line pattern with vertical stripes on the sidewall, SEM profile is generally consistent with 3D sidewall shape. The AFM-based LER measurement technique is useful as LER reference metrology to evaluate other LER measurement techniques.

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Section: Discussion On Ler Reference Metrologymentioning

confidence: 99%

Direct comparison of line edge roughness measurements by SEM and a metrological tilting-atomic force microscopy for reference metrology

Kizu

Misumi

Hirai

et al. 2020

J. Micro/Nanolith. MEMS MOEMS

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“…7). Giannatou et al [106] proposed the residual learning CNN (SEMD) for noise removal in scanning electron microscopic images. SEMD is a residual learning method inspired by the DnCNN and trained to estimate the noise at each pixel of a noisy image.…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

165

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Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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“…The DnCNN can be modified into a new network model to meet different image requirements. For example, suitably optimised DnCNN method can be successfully used for denoising of scanning electron microscopy (SEM) images 16,17 . This also improves the accuracy nanometre‐scale SEM measurements.…”

Section: Related Workmentioning

confidence: 99%

Imaging error compensation method for through‐focus scanning optical microscopy images based on deep learning

Jia-jun

Peng

et al. 2021

Journal of Microscopy

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Through-focus scanning optical microscopy (TSOM) is a model-based nanoscale metrology technique which combines conventional bright-field microscopy and the relevant numerical simulations. A TSOM image is generated after throughfocus scanning and data processing. However, the mechanical vibration and optical noise introduced into the TSOM image during image generation can affect the measurement accuracy. To reduce this effect, this paper proposes a imaging error compensation method for the TSOM image based on deep learning with U-Net.Here, the simulated TSOM image is regarded as the ground truth, and the U-Net is trained using the experimental TSOM images by means of a supervised learning strategy. The experimental TSOM image is first encoded and then decoded with the U-shaped structure of the U-Net. The difference between the experimental and simulated TSOM images is minimised by iteratively updating the weights and bias factors of the network, to obtain the compensated TSOM image. The proposed method is applied for optimising the TSOM images for nanoscale linewidth estimation. The results demonstrate that the proposed method performs as expected and provides a significant enhancement in accuracy.

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Deep learning denoising of SEM images towards noise-reduced LER measurements

Cited by 39 publications

References 7 publications

Direct comparison of line edge roughness measurements by SEM and a metrological tilting-atomic force microscopy for reference metrology

Direct comparison of line edge roughness measurements by SEM and a metrological tilting-atomic force microscopy for reference metrology

Methods for image denoising using convolutional neural network: a review

Imaging error compensation method for through‐focus scanning optical microscopy images based on deep learning

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