Investigation of the performance of state-of-the-art defect inspection tools within EUV lithography

Heuvel, Dieter Van den; Jonckheere, R.; Baudemprez, Bart; Cheng, Stone; Marcuccilli, Gino; Cross, Andrew; Inderhees, Gregg; Parisi, Paolo

doi:10.1117/12.916979

Cited by 4 publications

(2 citation statements)

References 5 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: 72%

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: 72%

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

“…High-sensitivity wafer inspection is extremely challenging because it is difficult to find and classify 10% delta-CD defects. 2 Inspecting a 13.5nm EUV mask with 193nm DUV is analogous to inspecting a 193nm mask with a mid-infrared wavelength of 2760nm. As such, DUV patterned mask defect inspection systems do not claim to detect defects with printing impact such as phase defects in the EUV multilayer.…”

Section: Introductionmentioning

confidence: 99%

Actinic patterned mask defect inspection for EUV lithography

Miyai

Kohyama

Suzuki

et al. 2019

Photomask Technology 2019

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As extreme ultraviolet (EUV) lithography enters high volume manufacturing, the semiconductor industry has considered a lithography-wavelength-matched actinic patterned mask inspection (APMI) tool to be a major remaining EUV mask infrastructure gap. Now, an actinic patterned mask inspection system has been developed to fill this gap. Combining experience gained from developing and commercializing the 13.5nm wavelength actinic blank inspection (ABI) system with decades of deep ultraviolet (DUV) patterned mask defect inspection system manufacturing, we have introduced the world's first high-sensitivity actinic patterned mask inspection and review system, the ACTIS A150 (ACTinic Inspection System). Producing this APMI system required developing and implementing new technologies including a highintensity EUV source and high-numerical aperture EUV optics. The APMI system achieves extremely high sensitivity to defects because of its high-resolution, low noise imaging. It has demonstrated a capability to detect mask defects having an estimated lithographic impact of 10% CD deviation on the printed wafer.

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Yield Challenges and Innovative Inspection & Metrology Solutions for Sub-10nm Manufacturing

Chen

Lenox

Saville

2019

2019 Electron Devices Technology and Manufacturing Conference (EDTM)

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Investigation of the performance of state-of-the-art defect inspection tools within EUV lithography

Cited by 4 publications

References 5 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

Actinic patterned mask defect inspection for EUV lithography

Yield Challenges and Innovative Inspection & Metrology Solutions for Sub-10nm Manufacturing

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