Aerial imaging study of the mask-induced line-width roughness of EUV lithography masks

Wojdyla, Antoine; Donoghue, Alexander; Benk, Markus P.; Naulleau, Patrick; Goldberg, Kenneth A.

doi:10.1117/12.2219513

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…While many defects are easily identified by eye, some defect and polarization combinations yield difference images that are visually similar. Benchmark photon densities ρ ph from the literature [36,37] for the wavelengths used in this study. λ (nm) 13 47 193 ρ ph (nm −2 ) 10 10 10 5 Confusion matrix for λ = 193 nm, ρ ph = 10000 nm −2 , X-polarization, CSR = 0.974 for multiple defect classification.…”

Section: Discussionmentioning

confidence: 99%

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Data-driven approaches to optical patterned defect detection

2019

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Computer vision and classification methods have become increasingly wide-spread in recent years due to ever-increasing access to computation power. Advances in semiconductor devices are the basis for this growth, but few publications have probed the benefits of data-driven methods for improving a critical component of semiconductor manufacturing, the detection and inspection of defects for such devices. As defects become smaller, intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection. These images are formed from the simulated scattering of realistic geometries with and without key defects while also taking into account line edge roughness (LER). LER is a known and challenging problem in fabrication as it yields additional scattering that further complicates defect inspection. Simulating images of an intentional defect array, a CNN approach is applied to extend detectability and enhance classification to these defects, even those that are more than 20 times smaller than the inspection wavelength.

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Section: Discussionmentioning

confidence: 99%

“…The photon densities that are applied here are based on current estimates for the intensity of the three wavelengths used in this study, see Refs. [36,37], and can be found in Tab. 1 below.…”

Section: Simulation Detailsmentioning

confidence: 99%

Data-driven approaches to optical patterned defect detection

2019

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“…Though resist line-edge roughness is generally governed by chemical processes (chemical shot noise and acidic diffusion), but the replication of the mask pattern roughness and replicated surface roughness or photon noise will still play a significant role when the pattern dimensions become constantly smaller [19].…”

Section: Challenges In Euv Lithographymentioning

confidence: 99%

The Challenges of Advanced CMOS Process from 2D to 3D

Radamson

Zhang

et al. 2017

Applied Sciences

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Abstract:The architecture, size and density of metal oxide field effect transistors (MOSFETs) as unit bricks in integrated circuits (ICs) have constantly changed during the past five decades. The driving force for such scientific and technological development is to reduce the production price, power consumption and faster carrier transport in the transistor channel. Therefore, many challenges and difficulties have been merged in the processing of transistors which have to be dealed and solved. This article highlights the transition from 2D planar MOSFETs to 3D fin field effective transistors (FinFETs) and then presents how the process flow faces different technological challenges. The discussions contain nano-scaled patterning and process issues related to gate and (source/drain) S/D formation as well as integration of III-V materials for high carrier mobility in channel for future FinFETs.

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“…This roughness is influenced by a complex interplay of factors within the photolithography process, including the lithographic source and mask optimization, 4 CD control, feature spacing, photoresist types and its processing. 5,6 The source and mask are particularly influential as they directly impact the aerial image and the dosage received by the photoresist, 7 ultimately determining the efficiency of the photoacid generator (PAG) in producing sufficient photoacid for exposure. The intrinsic physical and chemical properties of the photoresist play a significant role in determining roughness.…”

Section: Introductionmentioning

confidence: 99%

Photoresist process simulation to study line edge roughness

Sha,

Yao,

Jiang

et al. 2024

Optical and EUV Nanolithography XXXVII

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Roughness cannot be ignored as feature sizes shrink with Moore's Law, since it has potential to influence the device's performance. The roughness is usually described by line edge roughness (LER) and line width roughness (LWR). LER is the deviation of a feature edge from its ideal shape and is defined as three times the standard deviation, the deviation from the average line width is defined as LWR. For a certain critical dimension (CD) and pitch, there are many factors that can contribute to the roughness in the lithography process, such as source, mask optimizations, photoresist types and its processing, etc. An in-depth insight of the roughness formation mechanisms is essential to improve LER.This study employs photoresist process simulation to analyze LER, offering an efficient alternative to silicon data collection. Simulation analysis is carried out to examine the key factors influencing LER, including quencher concentration, photoacid diffusion length, PEB temperature. Concurrently, the study also delves into the impact of photoresist resin molecular composition and the development process on roughness. By using simulation to understand and predict roughness, the research provides insights into optimizing lithography parameters, thereby improving process stability and minimizing roughness formation.

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Aerial imaging study of the mask-induced line-width roughness of EUV lithography masks

Cited by 4 publications

References 8 publications

Data-driven approaches to optical patterned defect detection

Data-driven approaches to optical patterned defect detection

The Challenges of Advanced CMOS Process from 2D to 3D

Photoresist process simulation to study line edge roughness

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