Dr. Xuefeng Zeng Profile

Dr. Xuefeng Zeng

Senior Application Consultant at Siemens EDA

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Publications (9)

Proceedings Article | 13 November 2024 Presentation

Monotonic machine learning for lithography retargeting layer generation by leveraging contour-based metrology

Yuansheng Ma, Haizhou Yin, Le Hong, Xuefeng Zeng, Xiaomei Li, Hongming Zhang, Jeongmi Lee, Xiaoyuan Qi, Neal Lafferty, Germain Fenger, George Lippincott, Jiechang Hou, Abdulrazaq Adams, Xima Zhang, Yuyang Sun, Danping Peng, Renyang Meng, Werner Gillijns

Proceedings Volume 13216, 132161V (2024) https://doi.org/10.1117/12.3034720

KEYWORDS: Etching, Machine learning, Metrology, Modeling, Semiconductor manufacturing, Data modeling, Contour extraction, Simulations, Scanning electron microscopy, Image processing

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Proceedings Article | 12 November 2024 Presentation + Paper

OPC and modeling solution to support 0.55NA EUV stitching

Qinglin Zeng, Dongbo Xu, Xuefeng Zeng, Werner Gillijns, Edita Tejnil, Yuyang Sun, Germain Fenger

Proceedings Volume 13215, 1321507 (2024) https://doi.org/10.1117/12.3034957

KEYWORDS: Optical proximity correction, Design, Extreme ultraviolet lithography, Semiconducting wafers, Data modeling, Scanners

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Proceedings Article | 10 April 2024 Presentation

Machine learning (ML) based SEM contour extraction accelerated by GPU for etch modeling application

Yuansheng Ma, Xuefeng Zeng, Yunxiang Wang, Haizhou Yin, Xiaoyuan Qi, Shibing Wang, Le Hong

Proceedings Volume PC12958, (2024) https://doi.org/10.1117/12.3012442

KEYWORDS: Scanning electron microscopy, Contour extraction, Modeling, Etching, Machine learning, Metrology, Data modeling, Contour modeling, Semiconductors, Industry

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Proceedings Article | 10 April 2024 Poster + Paper

Contour metrology for process matching and OPC qualification with machine learning-based site selection

Jae Yeol Maeng, Soo Kheng Tan, Yee Mei Foong, Xuefeng Zeng, Shibing Wang, Steven Lubin, Robin Chia, Angeline Chung, Ken Jantzen, Yuyang Sun

Proceedings Volume 12955, 129553N (2024) https://doi.org/10.1117/12.3012681

KEYWORDS: Metrology, Scanning electron microscopy, Contour extraction, Machine learning, Optical proximity correction, Image processing, Electronic design automation, Design

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Proceedings Article | 30 April 2023 Presentation

A ML based OPC model pattern down-selection method with mask and wafer contours

Xuefeng Zeng, Yuansheng Ma, Yixiao Zhang, Yuyang Sun

Proceedings Volume PC12499, PC1249905 (2023) https://doi.org/10.1117/12.2662778

KEYWORDS: Semiconducting wafers, Photomasks, Optical proximity correction, Scanning electron microscopy, Calibration, Optical simulations, Machine learning, Wafer-level optics, Semiconductors, Semiconductor manufacturing

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Proceedings Article | 22 February 2021 Presentation + Paper

Stochastic defect criticality prediction enabled by physical stochastic modeling and massive metrology

ChangAn Wang, Peigen Cao, Maxence Delorme, Jen-Yi Wuu, Jiyou Fu, Fuming Wang, Bob Lin, Yiqiong Zhao, Yi-Hsing Peng, Yongfa Fan, Mu Feng, Bin Cheng, Jen-Shiang Wang, Mark Simmoms, Stefan Hunsche, Oliver Patterson, Kuo-Feng Pao, Abdalmohsen Elmalk, Kevin Gao, Ruochong Fei, Xuefeng Zeng, Xiaolong Zhang

Proceedings Volume 11609, 1160916 (2021) https://doi.org/10.1117/12.2584767

KEYWORDS: Stochastic processes, Pattern recognition, Semiconducting wafers, Wafer inspection, Inspection, Defect detection, Metrology, Failure analysis, Data modeling, Calibration

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Proceedings Article | 12 April 2016 Paper

Automated klarf-based defect inspection by electron-beam inspection tool: a novel approach to inline monitoring and/or process change validation

Na Cai, Xuefeng Zeng, Kevin Wu, Ho Young Song, Weihong Gao, Qing Tian, Chris Lei, Kewen Gao, Liuchen Wang, Yan Zhao

Proceedings Volume 9778, 977834 (2016) https://doi.org/10.1117/12.2218887

KEYWORDS: Inspection, Scanning electron microscopy, Copper, Semiconducting wafers, Defect inspection, Optical inspection, Back end of line, Chemical mechanical planarization, Electrons, Wafer inspection

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Proceedings Article | 25 March 2016 Paper

Net tracing and classification analysis on E-beam die-to-database inspection

Weihong Gao, Xuefeng Zeng, Peter Lin, Yan Pan, Ho Young Song, Hoang Nguyen, Na Cai, Zhijin Chen, Khurram Zafar

Proceedings Volume 9778, 97783Q (2016) https://doi.org/10.1117/12.2235347

KEYWORDS: Inspection, Optical proximity correction, Image classification, Semiconductors, Virtual colonoscopy, Diffusion, Metals, Data analysis, Failure analysis, Defect inspection, Ions, Electron beams, Defect detection, Back end of line

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A novel classification methodology is constructed for Electron Beam (E-Beam) die-to-database (D2DB) inspection results on contact and via layers. It is a design guided defects classification flow that helps to pin-point true defects from a large amount of false alarm defects. Die-to-database E-beam inspection has remarkable features that can help find systematic defects such as Damaged Via and Missing Via; which will be reported as DVC (Dark Voltage Contrast) defects. However, the D2DB result usually reports millions of defects that lie on both ‘active via’ and ‘floating via’, the former being defects-of-interest (DOI), and the latter being of little significance. The indiscriminant mixture of DOI (on active vias) and nuisance (on floating vias) is a challenge in the use of D2DB for finding systematic via defects. We overcome this challenge by overlaying the E-beam defect location onto the design layout file (GDS or OASIS) and tracing the path of the via to determine whether or not it connects to the active or diffusion layer. Our proposed flow uses Net Tracing Classification (NTC) feature in Anchor Hotspot Solution (AHS) to classify all the reported DVC defects into different groups, according to the electrical connectivity of the contact. This classification involves multiple interconnected process layers. All the reported DVC defects will be classified into three groups: (1) Real DVC defects, in which the net traces down to active layer; (2) False DVC type 1, in which the net traces down to gate (which is always dark); (3) False DVC type 2, in which the net traces down to floating metal (which is always dark as well). This enhanced defect classification is greatly helpful in separating real DVC contact/via defects from false alarms. It has a secondary benefit of reducing the total number of defects, which is helpful for subsequent in-depth data analysis. In addition, the verified real DVC locations can be used to generate care areas for E-Beam die-to-die (D2D) inspection, which can effectively improve throughput and reduce the turn-around-time (TAT). In this paper, we will discuss a use case at the Vx layer.

Proceedings Article | 19 February 2009 Paper

Enhancing lab-on-a-chip performance via tunable parallel liquid mircolens arrays

Ye Liu, Xuefeng Zeng, Liang Dong, Hongrui Jiang

Proceedings Volume 7167, 71670L (2009) https://doi.org/10.1117/12.809588

KEYWORDS: Microlens, Luminescence, Liquids, Microfluidics, Microlens array, Signal detection, Interfaces, Lab on a chip, Control systems, Pathogens

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Showing 5 of 9 publications

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