Dr. Jun Chen Profile

Dr. Jun Chen

at ASML

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Publications (3)

Proceedings Article | 17 May 2019 Paper

Metrology and deep learning integrated solution to drive OPC model accuracy improvement

Wei Yuan, Yifei Lu, Yuhang Zhao, Shoumian Chen, Ming Li, Hongmei Hu, Shuxin Yao, Zhunhua Liu, Qiaoqiao Li, Yu Tian, Zhiquan Zhou, Lirong Gu, Jinze Wang, Xichen Sheng, Guanyong Yan, Yazhong Zheng, Yueliang Yao, Yanjun Xiao, Liang Liu, Qian Zhao, Mu Feng, Jun Chen, Jun Lang

Proceedings Volume 10961, 109610N (2019) https://doi.org/10.1117/12.2516236

KEYWORDS: Metrology, Calibration, Data modeling, Scanning electron microscopy, Optical proximity correction, Performance modeling, Critical dimension metrology, Semiconducting wafers, Image processing, Photomasks

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Proceedings Article | 20 March 2019 Presentation + Paper

Compact modeling of negative tone development resist with photo decomposable quencher

Ao Chen, Kar Kit Koh, Yee Mei Foong, Bradley Morgenfeld, Jun Chen, Sandra Lee, Xi Chen, Hesham Omar, Mu Feng, ChangAn Wang, Keith Gronlund, Jun Lang, James Guerrero, Yiqiong Zhao

Proceedings Volume 10961, 109610F (2019) https://doi.org/10.1117/12.2514784

KEYWORDS: Diffusion, SRAF, Photoresist processing, Calibration, Photo decomposable quencher, Semiconducting wafers, Process modeling, Performance modeling, 3D modeling, Error analysis

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Proceedings Article | 28 April 2017 Presentation

CD-SEM metrology and OPC modeling for 2D patterning in advanced technology nodes (Conference Presentation)

Thomas Wallow, Chen Zhang, Anita Fumar-Pici, Jun Chen, Bart Laenens, Christopher Spence, David Rio, Paul van Adrichem, Harm Dillen, Jing Wang, Peng-Cheng Yang, Werner Gillijns, Patrick Jaenen, Frieda van Roey, Jeroen van de Kerkhove, Sergey Babin

Proceedings Volume 10145, 101451Q (2017) https://doi.org/10.1117/12.2260443

KEYWORDS: Metrology, Optical proximity correction, Data modeling, Optical lithography, Signal to noise ratio, OLE for process control, Instrument modeling, Image analysis, Calibration, Metals

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In the course of assessing OPC compact modeling capabilities and future requirements, we chose to investigate the interface between CD-SEM metrology methods and OPC modeling in some detail. Two linked observations motivated our study: 1) OPC modeling is, in principle, agnostic of metrology methods and best practice implementation. 2) Metrology teams across the industry use a wide variety of equipment, hardware settings, and image/data analysis methods to generate the large volumes of CD-SEM measurement data that are required for OPC in advanced technology nodes. Initial analyses led to the conclusion that many independent best practice metrology choices based on systematic study as well as accumulated institutional knowledge and experience can be reasonably made. Furthermore, these choices can result in substantial variations in measurement of otherwise identical model calibration and verification patterns. We will describe several experimental 2D test cases (i.e., metal, via/cut layers) that examine how systematic changes in metrology practice impact both the metrology data itself and the resulting full chip compact model behavior. Assessment of specific methodology choices will include: • CD-SEM hardware configurations and settings: these may range from SEM beam conditions (voltage, current, etc.,) to magnification, to frame integration optimizations that balance signal-to-noise vs. resist damage. • Image and measurement optimization: these may include choice of smoothing filters for noise suppression, threshold settings, etc. • Pattern measurement methodologies: these may include sampling strategies, CD- and contour- based approaches, and various strategies to optimize the measurement of complex 2D shapes. In addition, we will present conceptual frameworks and experimental methods that allow practitioners of OPC metrology to assess impacts of metrology best practice choices on model behavior. Finally, we will also assess requirements posed by node scaling on OPC model accuracy, and evaluate potential consequences for CD-SEM metrology capabilities and practices.

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