The cross talk of multi-errors impact on lithography performance and the method of its control

Li, Yanqiu; Han, Chunying; Guo, Xuejia; Liu, Lihui; Wang, Xuxia; Yang, Jianhong

doi:10.1117/12.978295

Cited by 2 publications

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“…1 Actually, the parameters related to the mask, process, and lithography tool simultaneously impact the lithography performance. 10,11 The lithography effects caused by multiple parameter errors could compensate for each other, 12 indicating that the co-optimization of multiple parameters could improve the PW. However, no effective methods have been published for co-optimizing the mask, process, and lithography-tool parameters.…”

Section: Introductionmentioning

confidence: 99%

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Guo

Dong

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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Abstract. Optimization technologies have been widely applied to improve lithography performance, such as optical proximity correction and source mask optimization (SMO). However, most published optimization technologies were performed under fixed process conditions, and only a few parameters were optimized. A method for mask, process, and lithography-tool parameter co-optimization (MPLCO) is developed to extend the process window. A normalized conjugate gradient algorithm is proposed to improve the convergence efficiency of the MPLCO when optimizing different scale parameters. In addition, a parametric mask and source are used in the MPLCO that could obtain exceedingly low mask and source complexity compared with a traditional SMO. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Guo

Dong

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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“…[1][2][3][4][5] Resolution in optical lithography obeys the Rayleigh resolution limit R ¼ k 1 ðλ∕NAÞ, where λ is the wavelength, NA is the numerical aperture, and k 1 is the process constant which can be minimized through RET methods. [6][7][8][9][10][11][12][13][14][15] Optical proximity correction (OPC) is one of the key RETs that modifies the mask pattern to precompensate for imaging distortions.…”

Section: Introductionmentioning

confidence: 99%

Fast pixel-based optical proximity correction based on nonparametric kernel regression

Song

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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Abstract. Optical proximity correction (OPC) is a resolution enhancement technique extensively used in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the layout is divided into small pixels, which are then iteratively modified until the simulated print image on the wafer matches the desired pattern. However, the increasing complexity and size of modern integrated circuits make PBOPC techniques quite computationally intensive. This paper focuses on developing a practical and efficient PBOPC algorithm based on a nonparametric kernel regression, a well-known technique in machine learning. Specifically, we estimate the OPC patterns based on the geometric characteristics of the original layout corresponding to the same region and a series of training examples. Experimental results on metal layers show that our proposed approach significantly improves the speed of a current professional PBOPC software by a factor of 2 to 3, and may further reduce the mask complexity. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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The cross talk of multi-errors impact on lithography performance and the method of its control

Cited by 2 publications

References 0 publications

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Fast pixel-based optical proximity correction based on nonparametric kernel regression

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