Dr. Jing Su Profile

Dr. Jing Su

at ASML

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Author

Publications (5)

Proceedings Article | 26 February 2021 Presentation

Full chip computational lithography for KrF multi-focal imaging (MFI) (Conference Presentation)

Will Conley, Vince Vince Plachecki, Stephen Hsu, Michael Crouse, Rongkuo Zhao, John He, Pieter Scheijgrond, Dezheng Sun, Xiaoyang Li, Dongqing Zhang, Ming-Chun Tien, Jun Ye, Rafael Howell, Chen Liu, Xiaolong Zhang, Hai Li, Zuanyi Li, Xiaobo Xie, Jing Su, Yzzer Roman

Proceedings Volume 11613, 116130E (2021) https://doi.org/10.1117/12.2585651

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Proceedings Article | 20 March 2018 Presentation + Paper

Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

Shibing Wang, Stanislas Baron, Nishrin Kachwala, Chidam Kallingal, Dezheng Sun, Vincent Shu, Weichun Fong, Zero Li, Ahmad Elsaid, Jin-Wei Gao, Jing Su, Jung-Hoon Ser, Quan Zhang, Been-Der Chen, Rafael Howell, Stephen Hsu, Larry Luo, Yi Zou, Gary Zhang, Yen-Wen Lu, Yu Cao

Proceedings Volume 10587, 105870N (2018) https://doi.org/10.1117/12.2299421

KEYWORDS: SRAF, Machine learning, Optical proximity correction, Photomasks, Lithography, Model-based design, Source mask optimization, Computational lithography, Image processing

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Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time.

Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO).

Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy.

ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges.

In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.

Proceedings Article | 16 October 2017 Presentation + Paper

Machine learning assisted SRAF placement for full chip

Shibing Wang, Jing Su, Quan Zhang, Weichun Fong, Dezheng Sun, Stanislas Baron, Cuiping Zhang, Chenxi Lin, Been-Der Chen, Rafael Howell, Stephen Hsu, Larry Luo, Yi Zou, Yen-Wen Lu, Yu Cao

Proceedings Volume 10451, 104510D (2017) https://doi.org/10.1117/12.2283493

KEYWORDS: SRAF, Machine learning, Lithography, Photomasks, Optical proximity correction, Model-based design, Computational lithography, Data modeling, Image processing

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SPIE Journal Paper | 24 August 2017

Imbalance aware lithography hotspot detection: a deep learning approach

Haoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, Bei Yu

JM3, Vol. 16, Issue 03, 033504, (August 2017) https://doi.org/10.1117/12.10.1117/1.JMM.16.3.033504

KEYWORDS: Lithography, Neural networks, Convolution, Neurons, Machine learning, Photomasks, Performance modeling, Feature extraction, Convolutional neural networks, Sensors

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Proceedings Article | 28 March 2017 Presentation + Paper

Imbalance aware lithography hotspot detection: a deep learning approach

Haoyu Yang, Luyang Luo, Jing Su, Chenxi Lin, Bei Yu

Proceedings Volume 10148, 1014807 (2017) https://doi.org/10.1117/12.2258374

KEYWORDS: Neurons, Photomasks, Convolutional neural networks, Sensors, Very large scale integration, Lithography, Convolution, Neural networks, Machine learning, Feature extraction, Data modeling, Design for manufacturing

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