Inspection of Stochastic Defects With Broadband Plasma Optical Systems for Extreme Ultraviolet (EUV) Lithography

Sah, Kaushik; Halder, Sandip; Cross, Andrew; Leray, Philippe

doi:10.1109/tsm.2019.2963483

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The most commonly observed defects are bridges and dislocations. Even EUVL is not free of defectivity issues, as noted in previous work, 4,5 and is shown to make bridge defects.…”

Section: Introductionmentioning

confidence: 70%

Defect recognition in line-space patterns aided by deep learning with data augmentation

Ahn

Kim

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

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Background: Finding optimal processing conditions to reduce defectivity is a major challenge in high-resolution lithographic tools such as directed self-assembly and extreme ultraviolet lithography.Aim: We aim to develop an efficient automated method that can detect defects and identify their types and locations, allowing for assessing the performance of lithographic processing conditions more easily.Approach: We propose a deep learning approach using an object recognition neural network for the classification and detection of defects in scanning electron microscopy (SEM) images featuring line-space patterns. We optimized our network by exploring its design variables and applied it on a case with limited numbers of SEM images available for training the network.Results: With an optimized network and data augmentation strategies (flipping and mixing images from simulations), it was possible to achieve a significant increase in the performance of the network. Transferability of the network was also proven when applied on a more diverse dataset of SEM images gathered from selected publications. Conclusions:The outcome of our work shows that the amalgamation of an optimal network design and augmentation strategies performs satisfactorily for defectivity analysis and is generic for data not constrained to fixed settings.

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“…The most commonly observed defects are bridges and dislocations. Even EUVL is not free of defectivity issues, as noted in previous work, 4,5 and is shown to make bridge defects.…”

Section: Introductionmentioning

confidence: 70%

Defect recognition in line-space patterns aided by deep learning with data augmentation

Ahn

Kim

et al. 2021

J. Micro/Nanopattern. Mats. Metro.

View full text Add to dashboard Cite

show abstract

“…The beam parameters , of the focused electron beam-induced process (FEBIP) in the deposition of SiO 2 from TEOS were optimized using CASINO simulations and gas-phase studies of the Si(OEt) 4 fragmentation pathways. Earlier EBID studies ,− ,− ,, report the deposition of Si(OEt) 4 at multiple temperatures, as a standalone and mixed with H 2 O, focusing on the behavior of the structures deposited at different temperatures.…”

Section: Introductionmentioning

confidence: 99%

Structural Analysis of Si(OEt)₄ Deposits on Au(111)/SiO₂ Substrates at the Nanometer Scale Using Focused Electron Beam-Induced Deposition

et al. 2023

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The focused electron beam-induced deposition (FEBID) process was used by employing a GeminiSEM with a beam characteristic of 1 keV and 24 pA to deposit pillars and line-shaped nanostructures with heights between 9 nm and 1 μm and widths from 5 nm to 0.5 μm. All structures have been analyzed to their composition looking at a desired Si/ O/C content measuring a 1:2:0 ratio. The C content of the structure was found to be ∼over 60% for older deposits kept in air (∼at room temperature) and less than 50% for later deposits, only 12 h old. Upon depositing Si(OEt) 4 at high rates and at a deposition temperature of under 0 °C, the obtained Si content of our structures was between 10 and 15 atom % (compositional percentage). The FEBID structures have been deposited on Au(111)/SiO 2 . The Au(111) was chosen as a substrate for the deposition of Si(OEt) 4 due to its structural and morphological properties. With its surface granulation following a Chevron pattern and surface defects having an increased contribution to the changes in the composition of the final structure content, the Au(111) surface characteristic behavior at the deposition of Si(OEt) 4 is an increase in the O ratio and a reduction in the nanodeposit heights.

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“…In this work, we will limit our scope to patterning defects arising out of lithography. In the past, it has been shown that the patterning defect process window is often limited by stochastic hotspots [2]. These hotspots have very low failure probabilities in a well-optimized process, and hence their detection necessitates large area sensitive defect inspection, such as with a broadband plasma (BBP) optical defect inspection system.…”

mentioning

confidence: 99%

Hotspot discovery and variability analysis for advanced EUV processes

Sah,

Chen,

Zhang

et al. 2024

Metrology, Inspection, and Process Control XXXVIII

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To maintain lithographic pitch scaling, extreme ultraviolet (EUV) processes have been adopted in high-volume manufacturing (HVM) for today's advanced logic and memory devices. Among various defect sources, stochastic patterning defects [1] are one of the most important yield detractors for EUV processes. In this work, we will limit our scope to patterning defects arising out of lithography. In the past, it has been shown that the patterning defect process window is often limited by stochastic hotspots [2]. These hotspots have very low failure probabilities in a well-optimized process, and hence their detection necessitates large area sensitive defect inspection, such as with a broadband plasma (BBP) optical defect inspection system. It has also been shown [2] that systematic issues in design can be exacerbated by stochastic variations. Hence, it is critical to discover these hotspots and study their variability with massive SEM metrology. Such analyses can uncover systematic trends, which can then be corrected and monitored. In this work, we discover hotspots using broadband plasma (BBP) optical inspection and study their variability using KLA's aiSIGHT™ pattern-centric defect and metrology software solution for automatic defect classification and SEM metrology measurements. We also demonstrate the need for fast and rigorous 3D probabilistic stochastic defect detection on design as a continuation of this work.

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Inspection of Stochastic Defects With Broadband Plasma Optical Systems for Extreme Ultraviolet (EUV) Lithography

Cited by 7 publications

References 8 publications

Defect recognition in line-space patterns aided by deep learning with data augmentation

Defect recognition in line-space patterns aided by deep learning with data augmentation

Structural Analysis of Si(OEt)₄ Deposits on Au(111)/SiO₂ Substrates at the Nanometer Scale Using Focused Electron Beam-Induced Deposition

Hotspot discovery and variability analysis for advanced EUV processes

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Inspection of Stochastic Defects With Broadband Plasma Optical Systems for Extreme Ultraviolet (EUV) Lithography

Cited by 7 publications

References 8 publications

Defect recognition in line-space patterns aided by deep learning with data augmentation

Defect recognition in line-space patterns aided by deep learning with data augmentation

Structural Analysis of Si(OEt)4 Deposits on Au(111)/SiO2 Substrates at the Nanometer Scale Using Focused Electron Beam-Induced Deposition

Hotspot discovery and variability analysis for advanced EUV processes

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Product

Resources

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Structural Analysis of Si(OEt)₄ Deposits on Au(111)/SiO₂ Substrates at the Nanometer Scale Using Focused Electron Beam-Induced Deposition