Determination of line edge roughness in low dose top-down scanning electron microscopy images

Verduin, T.; Kruit, P.; Hagen, C. W.

doi:10.1117/12.2046493

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…In Ref. 28, the authors addressed off-line metrology to study the acceptable noise level by decreasing the dose in simulated top-down SEM-like images. They also quantified the limit of required edge-based images for estimating LER-related parameters.…”

Section: Related Workmentioning

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%

“…In Ref. 28, authors used synthetic images as well as randomly generated line edges. They also processed those images by adding Poisson-distributed noise to every pixel of the noisefree images, after choosing an average electron density.…”

Section: Related Workmentioning

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 demonstrate this, 80 we applied the profile based edge-detection method of Ref. [5] to all four SEM images. The result of the roughness characterization (the 3σ LER), including the estimation for the correlation length (ξ) and roughness exponent (α) is given in indeed not sensitive to the introduced changes in the scattering cross-sections 3 .…”

Section: Model Sensitivity Analysismentioning

confidence: 99%

“…Nevertheless, computation time can still be a problem. A practical example is the determination of line edge roughness (LER) using the power spectral density (PSD), which requires the simulation of multiple images [5]. Recently, we have reduced the computation time further by rewriting the GEANT4 extension from FEI company, see Ref.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of secondary electron yields and SEM images to scattering parameters in MC simulations

Verduin

Lokhorst

Hagen

et al. 2016

Microelectronic Engineering

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In the simulation of secondary electron yields (SEY) and secondary electron microscopy (SEM) images, there is always the question: "are we using the correct scattering cross-sections?". The three scattering processes of interest are quasi-elastic phonon scattering, elastic Mott scattering and inelastic scattering using the dielectric function model. We have artificially scaled the scattering cross-sections, such that the probability for events associated with a particular model is either increased or decreased. The influence of this adjustment on the calculated SEYs and simulated SEM images is then evaluated. At first we have investigated the influence on the calculated SEY of pure and infinitely thick silicon. We have observed that the influence of the (quasi-elastic) acoustic phonon scattering cross-sections is seen all the way up to the incident primary electron energy of 10 keV. We have extended the analysis to the simulation of SEM images of three dimensional rough lines of PMMA located on a silicon substrate. We conclude that the scaling of the scattering cross-sections affects the contrast of the SEM images, but not the roughness characterization of the lines, i.e. the 3σ of the LER, correlation length and roughness exponent.

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“…Here we explain how this comes about as a result of FEBID: Figure 2 a,b show simulated SE images of two sets of dense FEBID lines at a centre to centre separation of 50 pixels and 30 pixels. The lines were generated as described in [ 9 ]. A random signal was generated to have a user-defined power spectral density (PSD).…”

Section: Introductionmentioning

confidence: 99%

Combined Focused Electron Beam-Induced Deposition and Etching for the Patterning of Dense Lines without Interconnecting Material

et al. 2020

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High resolution dense lines patterned by focused electron beam-induced deposition (FEBID) have been demonstrated to be promising for lithography. One of the challenges is the presence of interconnecting material, which is often carbonaceous, between the lines as a result of the Gaussian line profile. We demonstrate the use of focused electron beam-induced etching (FEBIE) as a scanning electron microscope (SEM)-based direct-write technique for the removal of this interconnecting material, which can be implemented without removing the sample from the SEM for post processing. Secondary electron (SE) imaging has been used to monitor the FEBIE process, and atomic force microscopy (AFM) measurements confirm the fabrication of well separated FEBID lines. We further demonstrate the application of this technique for removing interconnecting material in high resolution dense lines using backscattered electron (BSE) imaging to monitor the process.

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Determination of line edge roughness in low dose top-down scanning electron microscopy images

Cited by 9 publications

References 15 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

Sensitivity of secondary electron yields and SEM images to scattering parameters in MC simulations

Combined Focused Electron Beam-Induced Deposition and Etching for the Patterning of Dense Lines without Interconnecting Material

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