Source mask optimization methodology (SMO) and application to real full chip optical proximity correction

Zhang, Dongqing; Chua, GekSoon; Foong, YeeMei; Zou, Yi; Hsu, Stephen; S, Baron; Mu, Feng; Liu, Huayu; Li, Zhipan; Schramm, Jessy; Yun, Tang; Babcock, Carl P.; Choi, B. C.; Roling, Stefan; Navarra, Alessandra; Fischer, T.; Leschok, Andre; Liu, Xiaofeng; Shi, Wei; Qiu, Jianhong; Dover, Russell

doi:10.1117/12.916614

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Tachyon SMO (source-mask optimization) has been a key enabler for the 20 nm node and beyond with 193 nm immersion lithography especially for ASML DOE (diffractive optical element) and FlexRay illumination shape optimization [1,2]. ASML NXE:3300 EUV scanners with a set of standard off-axis illumination pupils have been introduced for the 10 nm node as of 2013 [3,4].…”

Section: Introductionmentioning

confidence: 99%

EUV source-mask optimization for 7nm node and beyond

Liu¹,

Howell²,

Hsu³

et al. 2014

SPIE Proceedings

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In this paper we introduce new source-mask co-optimization (SMO) capabilities for EUV with specific support of the details of imaging with NXE:33x0 scanners. New algorithms have been developed that fully exploit the adjustability of the light distribution inside the NXE:33x0 flexible illuminator, FlexPupil. The fast NXE M3D+ model accurately predicts the reflective 3D mask effects and enables novel pupil symmetries and mask defocus optimization. This mitigates the H-V bias, Bossung tilt, and pattern shift caused by shadowing and non-telecentricity, and reduces the sensitivity to flare. New pupil optimization flows will be shown. The optimized pupils are fully compliant with NXE:33x0 scanner specifications. We will demonstrate enhanced imaging performance of this NXE specific SMO on 7 nm node logic cut masks and show benefits up to 20% improved CD uniformity, and a reduction in the maximum pattern shifts.

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Section: Introductionmentioning

confidence: 99%

EUV source-mask optimization for 7nm node and beyond

Liu¹,

Howell²,

Hsu³

et al. 2014

SPIE Proceedings

Self Cite

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“…Additionally, one of the limitations of this approach is that the cluster number must be set manually before initiating the pattern clustering process. In contrast, the ASML Co. Ltd. (Veldhoven, The Netherlands) pattern selection technique is based on diffraction signatures [18,21]. This method has already been integrated into commercial computational lithography software, Tachyon (Denver, Colorado).…”

Section: Introductionmentioning

confidence: 99%

Critical Pattern Selection Method Based on CNN Embeddings for Full-Chip Optimization

Zhang,

Liu,

Zhou

et al. 2023

Photonics

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Source mask optimization (SMO), a primary resolution enhancement technology, is one of the most pivotal technologies for enhancing lithography imaging quality. Due to the high computation complexity of SMO, patterns should be selected by a selection algorithm before optimization. However, the limitations of existing selection methods are twofold: they are computationally intensive and they produce biased selection results. The representative method having the former limitation is the diffraction signature method. And IBM’s method utilizing the rigid transfer function tends to cause biased selection results. To address this problem, this study proposes a novel pattern cluster and selection algorithm architecture based on a convolutional neural network (CNN). The proposed method provides a paradigm for solving the critical pattern selection problem by CNN to transfer patterns from the source image domain to unified embeddings in a K-dimensional feature space, exhibiting higher efficiency and maintaining high accuracy.

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“…The cost function of SMO typically measures the image fidelity in terms of edge placement errors (EPEs) or the deviation of the printed image from the desired one [4,5], and evaluates the process robustness by depth of focus (DoF) [6], process window or other factors as deemed appropriate [7]. This objective function can also strike a moderate balance between the conflicting optimization performances, such as the small mask error enhancement factor (MEEF) and large DoF [8]. Moreover, source optimization during SMO provides more flexibility regarding both the source profile and its intensity [9,10], improving the process margin on the wafer.…”

Section: Introductionmentioning

confidence: 99%

Efficient source and mask optimization with augmented Lagrangian methods in optical lithography

Li¹,

Liu²,

Lam³

2013

Opt. Express

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Source mask optimization (SMO) is a powerful and effective technique to obtain sufficient process stability in optical lithography, particularly in view of the challenges associated with 22 nm process technology and beyond. However, SMO algorithms generally involve computation-intensive nonlinear optimization. In this work, a fast algorithm based on augmented Lagrangian methods (ALMs) is developed for solving SMO. We first convert the optimization to an equivalent problem with constraints using variable splitting, and then apply an alternating minimization method which gives a straightforward implementation of the algorithm. We also use the quasi-Newton method to tackle the sub-problem so as to accelerate convergence, and a tentative penalty parameter schedule for adjustment and control. Simulation results demonstrate that the proposed method leads to faster convergence and better pattern fidelity.

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Source mask optimization methodology (SMO) and application to real full chip optical proximity correction

Cited by 11 publications

References 8 publications

EUV source-mask optimization for 7nm node and beyond

EUV source-mask optimization for 7nm node and beyond

Critical Pattern Selection Method Based on CNN Embeddings for Full-Chip Optimization

Efficient source and mask optimization with augmented Lagrangian methods in optical lithography

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