Applicability of global source mask optimization to 22/20nm node and beyond

Tian, Kehan; Fakhry, Moutaz; Dave, Aasutosh; Tritchkov, Alexander; Tirapu-Azpiroz, Jaione; Rosenbluth, Alan E.; Melville, David O. S.; Sakamoto, Masaharu; Inoue, Takuya; Mansfield, Scott; Wei, Alexander; Kim, Young; Durgan, Bruce; Adam, Kostas; Berger, Gabriel; Bhatara, Gandharv; Meiring, Jason; Haffner, Henning; Kim, Byung‐Sung

doi:10.1117/12.879703

Cited by 7 publications

(6 citation statements)

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“…57,58 This type of mask engineering must be combined with simultaneous optimization and precise control of the illumination incident on the mask to create the required intensity distribution at the wafer. 59,60 In fact, the complexity of the coupled illumination-mask diffraction problem is so great that each future generation of chips relies on the computing power made available by the current generation of devices to solve it and, for all but the highest-volume devices, the mask cost is the dominant factor in the cost of ownership. 61,62 In addition, multiple masks may be required to print the features for a single level when double- or multiple-patterning approaches are used to achieve the desired feature density.…”

Section: Photolithographymentioning

confidence: 99%

Nanomanufacturing: A Perspective

2016

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Nanomanufacturing, the commercially-scalable and economically-sustainable mass production of nanoscale materials and devices, represents the tangible outcome of the nanotechnology revolution. In contrast to those used in nanofabrication for research purposes, nanomanufacturing processes must satisfy the additional constraints of cost, throughput, and time to market. Taking silicon integrated circuit manufacturing as a baseline, we consider the factors involved in matching processes with products, examining the characteristics and potential of top-down and bottom-up processes, and their combination. We also discuss how a careful assessment of the way in which function can be made to follow form can enable high-volume manufacturing of nanoscale structures with the desired useful, and exciting, properties.

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Section: Photolithographymentioning

confidence: 99%

Nanomanufacturing: A Perspective

2016

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“…Approaches in the industry do vary, but typically take on something similar to this flow. 151 Instead of using SMO mask solutions in the final mask, the improvement can be feedback to into RET development processors as learning for the main feature design and assist feature placement. 136,141 This is done because running a full chip optimization in SMO is still computationally or time bound (a point covered in Sec.…”

Section: A Using Intensive Optimization To Develop a Solutionmentioning

confidence: 99%

“…10(d)]. 151 Here, the OPC solution suffers from line end collapse and bridging due to low image slope in the patterning image, and hence large LER around the line-end. 145 Figure 10(d) shows the simulated PV-band performance at the locations established in Fig.…”

Section: B Benefit Of Source Optimizationmentioning

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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“…Source mask optimization (SMO) as one of the RETs becomes critical in 22 nm technological node and beyond since it provides a viable and powerful approach to scale down the resolution [2]. This is because highly customized sources are available by using diffractive optical element (DOE) or programmable illumination, which can shape the light to free-form with little throughput loss [3,4]. At the same time, the SMO process is carried out by various algorithms including the gradient-based method, the genetic algorithm, and more recently the augmented Lagrangian method for speed enhancement [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Efficient source mask optimization with Zernike polynomial functions for source representation

Wu¹,

Liu²,

Li³

et al. 2014

Opt. Express

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In 22nm optical lithography and beyond, source mask optimization (SMO) becomes vital for the continuation of advanced ArF technology node development. The pixel-based method permits a large solution space, but involves a time-consuming optimization procedure because of the large number of pixel variables. In this paper, we introduce the Zernike polynomials as basis functions to represent the source patterns, and propose an improved SMO algorithm with this representation. The source patterns are decomposed into the weighted superposition of some well-chosen Zernike polynomial functions, and the number of variables decreases significantly. We compare the computation efficiency and optimization performance between the proposed method and the conventional pixel-based algorithm. Simulation results demonstrate that the former can obtain substantial speedup of source optimization while improving the pattern fidelity at the same time.

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Applicability of global source mask optimization to 22/20nm node and beyond

Cited by 7 publications

References 0 publications

Nanomanufacturing: A Perspective

Nanomanufacturing: A Perspective

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

Efficient source mask optimization with Zernike polynomial functions for source representation

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