Contour based metrology: “make measurable what is not so"

Le-Gratiet, Bertrand; Bouyssou, R.; Ducoté, J.; Ostrovsky, Alain; Beylier, Charlotte; Gardin, Christian; Schuch, Nivea G.; Annezo, Vincent; Schneider, Lea; Milléquant, Matthieu; Petroni, Paolo; Figueiro, Thiago; Schiavone, Patrick

doi:10.1117/12.2551907

Cited by 8 publications

(7 citation statements)

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“…The proposed methodology relies on robust-to-noise contour extraction algorithms [4]. In this paper, an original method to evaluate and validate contour extraction algorithms, in the context of roughness measurement, is proposed.…”

Section: Roughness Measurement Of 2d Curvilinear Patterns: Challenges and Advanced Methodologymentioning

confidence: 99%

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

Pradelles¹,

Perraud²,

Fay³

et al. 2021

Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV

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Section: Roughness Measurement Of 2d Curvilinear Patterns: Challenges and Advanced Methodologymentioning

confidence: 99%

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

Pradelles¹,

Perraud²,

Fay³

et al. 2021

Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV

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“…Besides of the improvements needed for 2D patterns metrology, contours bring many new advantages compared to standard metrology. One advantage than must be highlighted is its capability of being an augmented metrology enabler [18]. First, its capacity of extracting the entirety of the edges information, makes possible the production of a much richer statistical data, increasing the quality for both local and global analysis.…”

Section: Introductionmentioning

confidence: 99%

The rise of contour metrology from niche solution to versatile enabler

Schuch

Robert

Figueiro

2023

Metrology, Inspection, and Process Control XXXVII

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The concept of using contours from SEM images for improving metrology results on semiconductor manufacturing applications exists for almost over two decades. However, the usage of contours was limited due to the difficulties encountered while identifying the contours and their consistency to be combined with traditional metrology results. As the technology for detecting contours advanced, so did the challenges, with higher levels of noise, lower contrast, more complex shapes, multiple layers combination, and tighter specifications. More recently, with the high level of sophistication attained by contour metrology, different applications and needs in different fields can be addressed by it. One of the most powerful benefits of using contour metrology is the possibility of combining information coming from the image to a layout, augmenting the metrology capability, pattern intent control and pattern quality. This work presents different strategies for obtaining contours from SEM images as well as multiple applications that are now relying on contours for either improving existing capabilities or proposing new ones that enable processes to continue to advance in future technology nodes.

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“…These two problems combined makes the EUV CD-SEM metrology very challenging. The industry is currently moving towards model-based contour extraction as a solution to handle complex 2D pattern measurements, especially on low SNR (signal to noise ratio) images [3][4][5][6][7][8][9]. However, for some applications, with extremely low SNR, denoising SEM images may prove a useful and complementary approach to model-based contour extraction.…”

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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Contour based metrology: “make measurable what is not so"

Cited by 8 publications

References 0 publications

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

Roughness measurement of 2D curvilinear patterns: challenges and advanced methodology

The rise of contour metrology from niche solution to versatile enabler

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

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