Denoising sample-limited SEM images without clean data

Lei, Hairong; Teh, Cho; Yu, Liangjiang; Fu, Gino; Pu, Lingling; Fang, Wei

doi:10.1117/12.2584653

Cited by 6 publications

(8 citation statements)

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“…Although the mapping from noisy pixels to noisy pixels was performed, the model will eventually converge due to the aforementioned premise. Building upon the idea of noise2nosie, Hairong Lei et al proposed a predesigned additive noise algorithm to augment and reduce the noise of input SEM images, which has proved to be very effective when the training dataset is small [10]. This algorithm adds various Gaussian noise level to the input images to increase the dataset as well as enhance the noise coverage of the input images, then it will train the denoiser by applying noise2noise model in either denoise-to-next or denoise-to-best scheme.…”

Section: Related Workmentioning

confidence: 99%

“…The thriving of ML has also quickly expanded to the semiconductor industry. ML and deep learning (DL) architectures and techniques have been applied in various tasks in the production line including overlay metrology [4]- [6], wafer leveling and alignment [7], defect detect and classification [8], [9], SEM images denoising [10], and mask optimization [11]- [15], and they have shown great improvement compared to conventional algorithms both in terms of accuracy and speed. Utilizing the advancement of ML techniques, we have proposed two methods to denoise SEM images for better analysis after measurement.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning based SEM image Denoising Approaches for Improving Metrology and Inspection of Thin Resists

Dey¹

2023

Preprint

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<p>As semiconductor manufacturing is moving towards 3 nm nodes and beyond, the development of advanced metrology techniques to control the quality of fabricated features on silicon wafers is becoming more and more crucial. Among the popular metrology methods, Scanning Electron Microscopy is one of the most important techniques since it can produce images with resolutions as low as a few nanometers. Thus, SEM is very suitable to inspect the critical dimension of nanoscale printed structures. However, CD-SEM images intrinsically contain a significant amount of background noise due to various sources. This background noise can lead to inaccurate metrology and erroneous defect detection. There is a significant requisite to reduce the noise signal in CD-SEM images while keeping the actual morphology of the pattern feature unaltered. In this paper, we proposed two deep learning-based methods to denoise SEM images, one based on (1) supervised/semi-supervised learning technique, and the other based on (2) unsupervised learning. The two proposed methods were experimented with different noisy SEM images of categorically different geometrical patterns and have demonstrated exceptional performance in reducing noise both qualitatively and quantitatively. We also have demonstrated how our proposed deep learning denoiser is applicable towards challenging application scenarios such as defect inspection and contour extraction, specifically with thin resists, with significantly improved accuracy. First, we have validated that our proposed denoising techniques, especially the unsupervised model, are effective. The unsupervised training scheme also requires single noisy acquisitions to train denoising CNNs without any ground-truth or synthetic images, against previous supervised/semi-supervised data greedy approaches. Second, the proposed deep learning denoiser assisted framework allows improved defect inspection and contour extraction with thin resists. The goal of this work is to establish a robust de-noising technique to reduce the dependency of SEM image acquisition settings and to extract repeatable and accurate CDSEM metrology information for high NA EUV. Finally, the limitations of our proposed approaches and how to overcome them were also thoroughly discussed. </p>

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning based SEM image Denoising Approaches for Improving Metrology and Inspection of Thin Resists

Dey¹

2023

Preprint

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“…To even further suppress the contribution of image noise to the error of the defect shape detection and generation, deep learningbased tools have proven to be quite successful. [17][18][19] Therefore, the question of whether deep learning-based methods can successfully be applied to mask repair applications is of high relevance.…”

Section: Mask Repairmentioning

confidence: 99%

“…[14][15][16] A general property of SEM images, the noise, has been addressed in further works, with goal of reducing the noise to obtain higher accuracy in the image analysis. [17][18][19] Other application fields of machine learning methods operating on wafer and layout data are hot spot detection, layout classification, and pattern similarity detection. [20][21][22] The methods are also used for tasks that are not targeted at the processing and use of image data.…”

Section: Introductionmentioning

confidence: 99%

Mask defect detection with hybrid deep learning network

Evanschitzky

Auth

Heil

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

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Background: Deep learning is a very fast-growing field in the area of artificial intelligence with remarkable results in recent years. Many works in the lithography and photomask field have shown progress of technology in this application area and the potential to improve lithography by the automation of processes. Despite this progress, the use of machine learning techniques in the field of mask repair still seems to be at the beginning.Aim: We show that deep learning-based methods can successfully be applied to mask repair applications.Approach: The presented system is a hybrid and modular approach based on a combination of several deep learning networks and analytical methods, enabling the detection of mask pattern defects and the determination of the exact defect shapes from SEM images. In the current version, the system is trained for line/space patterns and contact patterns with typical defect types. The modularity allows for the extensibility to new use cases. The issue of an insufficient amount of training data is addressed using purely computer-generated simplified SEM data in combination with a specific network architecture. Results:The very good functionality and defect detection accuracy of the system are demonstrated with a set of real SEM images with line/space patterns and contact patterns with numerous defects. In particular, a 100% true defect detection rate could be obtained. Conclusions:The presented machine learning approach demonstrates the successful defect identification, location, and shape determination from real mask SEM images.

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“…Nevertheless, such algorithms, especially the deep learning (DL) algorithms, generally require many images. Moreover, they usually require the low frame images as inputs and the associated high frame images (or ground truth) as outputs, in order to train the Neural Network (NN) model [10,13,14,17,18].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset

Moly

Schuch

Robert

et al. 2023

Metrology, Inspection, and Process Control XXXVII

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Low-noise CD-SEM images are required in order to obtain robust and reliable measurement results, especially for complex 2D patterns. However, standard practices to reduce CD-SEM noise during image acquisition (increasing number of frames, increasing beam current, etc.) also increase acquisition time and the probability of deteriorating the materials under inspection. This effect is getting further attention of the industry on the case of EUV (extreme ultraviolet) resist, being an electron sensitive material and presenting small thickness, requiring extra care in order to prevent damage. Although there are techniques that intrinsically improve the metrology robustness to noise (such as model-based contour extraction), denoising SEM images may prove useful and a complementary approach for further improve metrology quality in such challenging cases. In this work we propose an innovative solution using Deep Learning Neural Network (DLNN) for low frame image denoising, which performs the model calibration using low frame images only, from a reduced dataset. A specific data augmentation approach is used in order to limit the number of images needed for the training. The denoising performance of the algorithm was evaluated in terms of accuracy and precision over a synthetic dataset, not used during the training of the neural network. The results show an improvement both in precision (up to 50% on the extreme case, 5% on average) as well in accuracy (over 13% on average).

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Denoising sample-limited SEM images without clean data

Cited by 6 publications

References 0 publications

Deep Learning based SEM image Denoising Approaches for Improving Metrology and Inspection of Thin Resists

Deep Learning based SEM image Denoising Approaches for Improving Metrology and Inspection of Thin Resists

Mask defect detection with hybrid deep learning network

Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset

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