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

Moly, Alexandre; Schuch, Nivea G.; Robert, Frédéric; Figueiro, Thiago; Bustos, J.; Perraud, L.; Pradelles, Jonathan; Sezestre, Élie

doi:10.1117/12.2659168

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…This method requires two networks to be trained simultaneously, making it difficult to control and less stable than the conventional deep-learning approach [3] . Therefore, in previous reports deeplearning is used rather than GAN, especially Noise2Noise (N2N) model [4,5,6] . The N2N model is an unsupervised learning method that leverages multiple noise images taken at the same location to effectively eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

Enhancing CD-SEM accuracy with attention-boosted Noise2Noise model

Okada,

Liu,

Lee

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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Semiconductor manufacturing relies on Critical Dimension Scanning Electron Microscopy (CD-SEM) for precision in resist pattern measurements. High-resolution CD-SEM images, while desirable, can damage the resist due to increased electron beam exposure with higher frame numbers. To address this, Noise2Noise, a deep-learning noise reduction method, is introduced. Noise2Noise employs multiple noise images for unsupervised noise reduction. However, it struggles with unknown samples and limited training data. This research enhances the Noise2Noise model by introducing Attention and Residual-Recurrent structures to extract high-precision images from low-resolution inputs (1 frame). The Attentionboosted Noise2Noise model in particular exhibits superior accuracy with improved Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) for unseen patterns. Overall, the modeling error characterized by (ΔCD/CD) has been reduced compared to the conventional Noise2Noise method, promising improved CD-SEM accuracy for advanced CMOS manufacturing.

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Section: Introductionmentioning

confidence: 99%

Enhancing CD-SEM accuracy with attention-boosted Noise2Noise model

Okada,

Liu,

Lee

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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Trends in e-beam metrology and inspection

Lorusso

2024

Metrology, Inspection, and Process Control XXXVIII

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For many years, e-beam SEM metrology has been quite conservative. However, a few years ago, we began to observe a sort of restlessness in the field, a trend that was consistently confirmed year after year since then. Such a change was mainly driven by the daunting specification requirements for the advanced metrology nodes and High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL), as well as by the device evolution in terms of architecture. We observed a systematic attempt to expand the application space in which the e-beam SEM had operated, tackling issues ranging from metrology of 3D structures to edge placement error, voltage contrast, e-beam inspection, and more.Such an expansion of the application space has forced heavily automated SEM to evolve into something different: a more flexible and powerful e-beam tool focusing not only on CD metrology, but on a range of novel use cases. To implement these new applications, it soon became evident that it was necessary to expand the range and type of tool specifications and features. We observed changes in landing energy, beam current, resolution, as well as algorithms. The detection process changed too, and higher current and better detection efficiency opened the door to a variety of back-scattered electron applications. Artificial intelligence is now critical not only in terms of speed of analysis, but most importantly for the quality of its results. Multibeam tools are fast approaching and have the potential to become a central technology element in the high-NA EUVL environment, where resolution and throughput for inspection must go hand-in-hand. In this paper, we provide an overview of the recent trends in e-beam metrology and inspection. e-beam, CD SEM, e-beam Inspection, Multibeam, e-beam Review Invited Paper

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Low landing energy as an enabler for optimal contour based OPC modeling in the EUV era

Alkoken,

Baram,

Oron

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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OPC (optical proximity correction) is a well-known and widely used RET (resolution enhancement technique) in optical lithography, which main purpose is improving pattern fidelity and process window. OPC relies on CD-SEM (critical dimension scanning electron microscope) images as a source of information for EPE (edge placement error) measurement, as input for the OPC modeling flow. However, as the pitch scales, stricter OPC modeling specifications, reduced metrology error budget, and increasing pattern complexities, create challenges for traditional CD-SEM metrology. Also, parameters like materials, landing energy and other scanning conditions, may have a different impact in the final image in terms of shrinkage, charging and distortion. All these effects need to be characterized during the modeling step to optimize OPC. Examples of EPE uniformity and pattern shrinkage are illustrated in Figure 1, for different scan conditions.With the increasing adoption of EUV (extreme ultraviolet) technology in the field of patterning and the upcoming introduction of High-NA (high numerical aperture) EUV, the need for further understanding of the EUV resist material interaction with a CD-SEM electron beam is also increasing [1]. Low landing energy scanning approach is a way to minimize EUV resist damage during image acquisition and provide a more accurate view of EUV lithography effects [2]. Also, to improve the design space coverage, resist (or lithographic) and etch modeling require thousands of measurements over different types of structures. Additionally, massive EPE statistics makes model building more robust [3]. A contour may provide the equivalent of hundreds of different measurementsusing the entirety of the contour instead of only relying on the placement of gauges, as in conventional measurement, further increases the sampling and the coverage of the produced model, which make contours the suitable choice [4,5]. The influence of low landing energy is expected to be seen in the etch modeling step, where it should have higher accuracy and lower etch bias when compared to litho SEM data. However, a drawback of using low landing energy for image acquisition is a lower SNR (Signal-to-Noise ratio) compared to standard imaging conditions. This may represent extra challenges to the contour extraction algorithm which needs to be robust. These challenges can be addressed by using a model-based approach [6,7].In this work, different CD-SEM scanning conditions were studied in order to find optimal working points for modeling purposes. Since different patterns have varied outcomes under different e-beam conditions, thus affecting the model residues, more than 600 distinct patterns from EUV lithography -going from 1D to complex 2D -were selected to provide enough sensitivity to when building the litho and the etch models. Contour metrology is used to detect the edges, for EPE analysis, and to provide inputs for the modeling flow. At last, one of the key aspects of this work is the use of low landing energy (150eV) scan approach to...

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

Cited by 3 publications

References 18 publications

Enhancing CD-SEM accuracy with attention-boosted Noise2Noise model

Enhancing CD-SEM accuracy with attention-boosted Noise2Noise model

Trends in e-beam metrology and inspection

Low landing energy as an enabler for optimal contour based OPC modeling in the EUV era

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