Dr. Moongyu Jeong Profile

Dr. Moongyu Jeong

Application Engineer Sr.Staff

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Author

Publications (11)

SPIE Journal Paper | 20 December 2023

Enhancing mask synthesis for curvilinear masks in full-chip extreme ultraviolet lithography

Kevin Hooker, Guangming Xiao, Yu-Po Tang, Yunqiang Zhang, Moongyu Jeong, John Valadez, Kevin Lucas

JM3, Vol. 22, Issue 04, 041606, (December 2023) https://doi.org/10.1117/12.10.1117/1.JMM.22.4.041606

KEYWORDS: Optical proximity correction, Extreme ultraviolet lithography, Lithography, Optical lithography, Machine learning, Extreme ultraviolet, Semiconducting wafers, Education and training, Industry, Source mask optimization

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Proceedings Article | 30 April 2023 Presentation

Machine learning based inverse lithography technology for an advanced DRAM contact layer

Moongyu Jeong, Hyunchul Kim, Kyle Braam, Munhoe Do, Kangjin Kim, Jongcheon Park, Chunsoo Kang, Chanha Park, Thomas Cecil

Proceedings Volume 12495, 124951E (2023) https://doi.org/10.1117/12.2657808

KEYWORDS: Machine learning, Lithography, Photomasks, Source mask optimization, Numerical analysis, Convolutional neural networks, Computational lithography

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Proceedings Article | 25 May 2022 Presentation + Paper

Curvilinear mask solutions for full-chip EUV lithography

Kevin Hooker, Guangming Xiao, Yu-Po Tang, Yunqiang Zhang, Moongyu Jeong, John Valadez, Kevin Lucas

Proceedings Volume 12054, 1205407 (2022) https://doi.org/10.1117/12.2618392

KEYWORDS: Photomasks, Optical proximity correction, Optical lithography, Lithography, Extreme ultraviolet, Extreme ultraviolet lithography, Machine learning, Semiconducting wafers, Manufacturing, Deep ultraviolet

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Proceedings Article | 22 February 2021 Presentation

Validation of machine learning OPC compact models for advanced manufacturing

Moongyu Jeong, Marco Guajardo, Cheng-En Wu, Song-haeng Lee, Tim Fuehner, Lena Zavyalova, Li-Jin Chen, Hua Song, Kevin Lucas

Proceedings Volume 11614, 116140R (2021) https://doi.org/10.1117/12.2584940

KEYWORDS: Optical proximity correction, Machine learning, Data modeling, Manufacturing, Critical dimension metrology, Semiconductors, Photomasks, Semiconducting wafers, Optical lithography, Neural networks

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SPIE Journal Paper | 15 November 2017

Pixel-based learning method for an optimized photomask in optical lithography

Moongyu Jeong, Jae Hahn

JM3, Vol. 16, Issue 04, 043504, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMM.16.4.043504

KEYWORDS: Photomasks, Optical lithography, Source mask optimization, SRAF, Detection and tracking algorithms, Photovoltaics, Semiconducting wafers, Optical proximity correction, Computer simulations, Model-based design

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SPIE Journal Paper | 17 March 2016

Prediction of biases for optical proximity correction through partial coherent identification

Moongyu Jeong, Jae Hahn

JM3, Vol. 15, Issue 01, 013509, (March 2016) https://doi.org/10.1117/12.10.1117/1.JMM.15.1.013509

KEYWORDS: Optical proximity correction, Bismuth, Lawrencium, Image segmentation, Model-based design, Detection and tracking algorithms, Neural networks, Optical lithography, Solids, Evolutionary algorithms

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Proceedings Article | 23 March 2011 Paper

Investigating the performance of directional boundary layer model through staged modeling method

Moon-Gyu Jeong, Won-Chan Lee, Seung-Hune Yang, Sung-Hoon Jang, Seong-Bo Shim, Young-Chang Kim, Chun-Suk Suh, Seong-Woon Choi, Young-Hee Kim

Proceedings Volume 7973, 79732F (2011) https://doi.org/10.1117/12.879604

KEYWORDS: Photomasks, Cadmium, Artificial intelligence, Optical proximity correction, Semiconducting wafers, Performance modeling, Neodymium, Data modeling, Error analysis, Statistical modeling

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Generally speaking, the models used in the optical proximity effect correction (OPC) can be divided into three parts, mask part, optic part, and resist part. For the excellent quality of the OPC model, each part has to be described by the first principles. However, OPC model can't take the all of the principles since it should cover the full chip level calculation during the correction. Moreover, the calculation has to be done iteratively during the correction until the cost function we want to minimize converges. Normally the optic part in OPC model is described with the sum of coherent system (SOCS[1]) method. Thanks to this method we can calculate the aerial image so fast without the significant loss of accuracy. As for the resist part, the first principle is too complex to implement in detail, so it is normally expressed in a simple way, such as the approximation of the first principles, and the linear combinations of factors which is highly correlated with the chemistries in the resist. The quality of this kind of the resist model depends on how well we train the model through fitting to the empirical data. The most popular way of making the mask function is based on the Kirchhoff's thin mask approximation. This method works well when the feature size on the mask is sufficiently large, but as the line width of the semiconductor circuit becomes smaller, this method causes significant error due to the mask topography effect. To consider the mask topography effect accurately, we have to use rigorous methods of calculating the mask function, such as finite difference time domain (FDTD[2]) and rigorous coupled-wave analysis (RCWA[3]). But these methods are too time-consuming to be used as a part of the OPC model. Until now many alternatives have been suggested as the efficient way of considering the mask topography effect. Among them we focused on the boundary layer model (BLM) in this paper. We mainly investigated the way of optimization of the parameters for the BLM since the feasibility of the BLM has been investigated in many papers[4][5][6]. Instead of fitting the parameters to the wafer critical dimensions (CD) directly, we tried to use the aerial image (AI) from the rigorous simulator with the electromagnetic field (EMF) solver. Usually that kind of method is known as the staged modeling method. To see the advantages of this method we conducted several experiments and observed the results comparing the method of fitting to the wafer CD directly. Through the tests we could observe some remarkable results and confirmed that the staged modeling had better performance in many ways.

Proceedings Article | 29 September 2010 Paper

Improving model prediction accuracy for ILT with aggressive SRAFs

Sunggon Jung, Woojoo Sim, Moongyu Jeong, Junghoon Ser, Sungwoo Lee, Seoung-woon Choi, Xin Zhou, Lan Luan, Thomas Cecil, Donghwan Son, Robert Gleason, David Kim

Proceedings Volume 7823, 782311 (2010) https://doi.org/10.1117/12.864528

KEYWORDS: SRAF, Optical proximity correction, Scanning electron microscopy, Calibration, Diffusion, Photomasks, Lithography, Performance modeling, Cadmium sulfide, Critical dimension metrology

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Proceedings Article | 22 March 2010 Paper

Comparison of OPC models with and without 3D-mask effect

Jung-Hoon Ser, Tae-Hoon Park, Moon-Gyu Jeong, Eun-Mi Lee, Sung-Woo Lee, Chun-Suk Suh, Seong-Woon Choi, Chan-Hoon Park, Joo-Tae Moon

Proceedings Volume 7640, 76401T (2010) https://doi.org/10.1117/12.848317

KEYWORDS: 3D modeling, Optical proximity correction, Photomasks, Cadmium, Semiconducting wafers, Scanning electron microscopy, Semiconductors, Electronics, Scanners, Lithography

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Proceedings Article | 10 March 2010 Paper

A simplified reaction-diffusion system of chemically amplified resist process modeling for OPC

Yongfa Fan, Moon-Gyu Jeongb, Junghoon Ser, Sung-Woo Lee, Chunsuk Suh, Kyo-Il Koo, Sooryong Lee, Irene Su, Lena Zavyalova, Brad Falch, Jason Huang, Thomas Schmoeller

Proceedings Volume 7640, 764039 (2010) https://doi.org/10.1117/12.846737

KEYWORDS: Data modeling, Process modeling, Diffusion, Optical proximity correction, Semiconducting wafers, Calibration, Photoresist processing, Fiber optic illuminators, Chemically amplified resists, Molecules

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Proceedings Article | 27 March 2007 Paper

Improving the model robustness for OPC by extracting relevant test patterns for calibration

Moon-Gyu Jeong, Sang-Ho Lee, Jee-Eun Jung, Chankyeong Hyon, Iljung Choi, Young-Seog Kang, Youngkyou Park

Proceedings Volume 6520, 652048 (2007) https://doi.org/10.1117/12.711806

KEYWORDS: Data modeling, Optical proximity correction, Calibration, Optical calibration, Model-based design, Statistical modeling, Optical lithography, Semiconducting wafers, Optical testing, Cadmium

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Showing 5 of 11 publications

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