A systematic study of source error in source mask optimization

Alleaume, C.; Yesilada, Emek; Farys, V.; Depre, L.; Arnoux, V.; Li, Zhipan; Trouiller, Yorick; Serebriakov, Alexander

doi:10.1117/12.864246

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Although DoPE J s of both masks are more than 5%, DoPE I s are less than 2%. Such phenomenon indirectly verifies that aerial images are quite insensitive to large defects on sources [41,42]. The above merit matches one of the characteristics of holograms that 3D images are reproduced well even some parts are damaged [43][44][45].…”

Section: Resultssupporting

confidence: 65%

Fast source optimization involving quadratic line-contour objectives for the resist image

Yu¹,

Yu²,

Chao³

2012

Opt. Express

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In Abbe's formulation, source optimization (SO) is often formulated into a linear or quadratic problem, depending on the choice of objective functions. However, the conventional approach for the resist image, involving a sigmoid transformation of the aerial image, results in an objective with a functional form. The applicability of the resist-image objective to SO or simultaneous source and mask optimization (SMO) is therefore limited. In this paper, we present a linear combination of two quadratic line-contour objectives to approximate the resist image effect for fast convergence. The line-contour objectives are based on the aerial image on drawn edges using a constant threshold resist model and that of pixels associated with an intensity minimum for side-lobe suppression. A conjugate gradient method is employed to assure the convergence to the global minimum within the number of iterations less than that of source variables. We further compare the optimized illumination with the proposed line-contour objectives to that with a sigmoid resist-image using a steepest decent method. The results show a 100x speedup with comparable image fidelity and a slightly improved process window for the two cases studied.

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Section: Resultssupporting

confidence: 65%

Fast source optimization involving quadratic line-contour objectives for the resist image

Yu¹,

Yu²,

Chao³

2012

Opt. Express

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“…108 However, the degrees of freedom explored was limited by the illumination hardware, which was often fixed by the hardware design to a 2-3 parameter variation (for example, inner-r, outer-r, and open angle). The performance of production PI has recently been reviewed, [111][112][113][114][115] demonstrating the qualification, monitoring, and integration of a PI. This allows lithographers to explore source designs of much greater complexity and leverage the potential to trade off critical lithographic performance.…”

Section: Programmable Illuminationmentioning

confidence: 99%

Computational lithography: Exhausting the resolution limits of 193-nm projection lithography systems

Melville¹,

Rosenbluth²,

Waechter³

et al. 2011

Journal of Vacuum Science & Technology B, Nanotechnology an

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Articles you may be interested inAnalysis and optimization approaches for wide-viewing-angle λ/4 plate in polarimetry for immersion lithographyIn the recent past, scaling of semiconductor fabrication systems has been dominated by wavelength and numerical aperture modifications. This is now no longer the case for 193-nm immersion projection lithography (193i) systems as there are no technical paths for continued benefit from the in these areas. Instead, a range of techniques including patterning processes and system optimization are being used to push the limits of the system. This paper will review the elements that are now driving scaling for a system of fixed wavelength and numerical aperture.

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“…However, all of these traditional SMO methods are based on an ideal lithography system, thus sensitive to the nonideal factors of the actual scanners. Recently, Alleaume et al studied the impact of the source errors on the pixelated and parametric SMO [9]. Their results indicated that the imaging quality of the freeform source resulting from pixelated SMO is more sensitive to the source errors than that of the parametric source, and the source blur seriously impacts the lithography performance.…”

Section: Introductionmentioning

confidence: 96%

Robust hybrid source and mask optimization to lithography source blur and flare

Han

et al. 2015

Appl. Opt.

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As a promising resolution enhancement technique, a set of pixelated source and mask optimization (SMO) methods has been introduced to further improve the lithography at 45 nm node and beyond. Recently, some papers studied the impact of the scanner errors on SMO, and the results revealed that the source blur and flare seriously impact on the lithography performance of the optimal source and mask resulting from SMO. However, current SMO methods did not propose an effective method to compensate for the impact of these nonideal factors of the actual scanners. To overcome this drawback, this paper focuses on developing a robust hybrid SMO (HSMO) method where the sensitivities of the aerial image to source blur and flare are introduced into the cost function. Simulation results are compared with traditional SMO to demonstrate the benefit of the proposed source blur-flare-aware HSMO method in pattern fidelity and process window.

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A systematic study of source error in source mask optimization

Cited by 4 publications

References 6 publications

Fast source optimization involving quadratic line-contour objectives for the resist image

Fast source optimization involving quadratic line-contour objectives for the resist image

Computational lithography: Exhausting the resolution limits of 193-nm projection lithography systems

Robust hybrid source and mask optimization to lithography source blur and flare

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