Virtual overlay metrology for fault detection supported with integrated metrology and machine learning

Lee, Honggoo; Schmitt-Weaver, Emil; Kim, Minsuk; Han, Sangjun; Kim, Myoung-Soo; Kwon, Won-Taik; Park, Sung-Ki; Ryan, Kevin; Theeuwes, Thomas; Sun, Kyu-Tae; Lim, Young-Wan; Slotboom, Daan; Kubis, Michael; Staecker, Jens

doi:10.1117/12.2085475

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…Cognitive learning (a type of ML) has also been used to speed up complex characterisation and analysis of IC features, such as object detection, classification, and automated measurements 156 . In another example, pre-exposure metrology data from ultraviolet level sensor of a lithography system was used to predict clamped wafer shape, and then hierarchical clustering with dendrograms provided insight on overlay 157,158 . Other interesting uses include autonomous probe tip monitoring and reconditioning, where a neural network was trained (by a small set of images) to identify isolated dangling bonds at the end of a tip and to apply electrical pulses to sharpen the tip 159 ; using ML to develop sampling strategies for OCD and XRF for electrical test prediction; and pattern analysis and prediction for automated design layout 160 .…”

Section: Emerging and Potentially Disruptive Technologiesmentioning

confidence: 99%

“…Other interesting uses include autonomous probe tip monitoring and reconditioning, where a neural network was trained (by a small set of images) to identify isolated dangling bonds at the end of a tip and to apply electrical pulses to sharpen the tip 159 ; using ML to develop sampling strategies for OCD and XRF for electrical test prediction; and pattern analysis and prediction for automated design layout 160 . Note that ML and related techniques could be implemented as part of established automated process control (APC) 161 and virtual metrology techniques currently used in the industry, and the information linked to factory wide data or applied to other metrology issues 138, 155, 157, 158, 162 Virtual metrology refers to “…the technology of prediction of post process metrology variables (either measurable or nonmeasurable) using process and wafer state information that could include upstream metrology and/or sensor data ” and would benefit from these techniques 163 .…”

Section: Emerging and Potentially Disruptive Technologiesmentioning

confidence: 99%

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Metrology for the next generation of semiconductor devices

Badaroglu²,

et al. 2018

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The semiconductor industry continues to produce ever smaller devices that are ever more complex in shape and contain ever more types of materials. The ultimate sizes and functionality of these new devices will be affected by fundamental and engineering limits such as heat dissipation, carrier mobility and fault tolerance thresholds. At present, it is unclear which are the best measurement methods needed to evaluate the nanometre-scale features of such devices and how the fundamental limits will affect the required metrology. Here, we review state-of-the-art dimensional metrology methods for integrated circuits, considering the advantages, limitations and potential improvements of the various approaches. We describe how integrated circuit device design and industry requirements will affect lithography options and consequently metrology requirements. We also discuss potentially powerful emerging technologies and highlight measurement problems that at present have no obvious solution.

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Section: Emerging and Potentially Disruptive Technologiesmentioning

confidence: 99%

Section: Emerging and Potentially Disruptive Technologiesmentioning

confidence: 99%

Metrology for the next generation of semiconductor devices

Badaroglu²,

et al. 2018

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“…ML and deep learning (DL) architectures and techniques have been applied in various tasks in the production line including overlay metrology, [3][4][5] wafer leveling and alignment, 6 defect detection and classification, 7,8 SEM images denoising, 9 and mask optimization, [10][11][12][13] and they have shown great improvement compared to conventional algorithms both in terms of accuracy and speed. In this research, we have demonstrated the application of our deep learning denoiser assisted framework towards enabling SEM contour extraction possible for all the edges in raw noisy DRAM SEM images with bit-line-periphery (BLP) and storage node landing pad (SNLP) patterns.…”

Section: Introductionmentioning

confidence: 99%

A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

Dey,

Wu,

Blanco

et al. 2023

38th European Mask and Lithography Conference (EMLC 2023)

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“…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%

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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Virtual overlay metrology for fault detection supported with integrated metrology and machine learning

Cited by 7 publications

References 1 publication

Metrology for the next generation of semiconductor devices

Metrology for the next generation of semiconductor devices

A deep learning facilitated approach for SEM image denoising towards improved contour extraction for 1D and 2D structures

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

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