Prof. Xu Ma Profile

Prof. Xu Ma

at Beijing Institute of Technology

SPIE Involvement:

Author

Publications (34)

SPIE Journal Paper | 31 October 2024

Robust source and polarization joint optimization for thick-mask lithography imaging

Shengen Zhang, Xu Ma, Gonzalo Arce

JM3, Vol. 23, Issue 04, 043201, (October 2024) https://doi.org/10.1117/12.10.1117/1.JMM.23.4.043201

KEYWORDS: Polarization, Mathematical optimization, Lithography, Image processing, Imaging systems, Algorithm development, Printing, Light sources and illumination, Diffraction, Matrices

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Proceedings Article | 18 July 2024 Paper

Mask correction for DMD-based lithography testbed with calibrated imaging model

Chaojun Huang, Xu Ma, Shengen Zhang, Jingwen Lei

Proceedings Volume 13179, 131791F (2024) https://doi.org/10.1117/12.3031601

KEYWORDS: Calibration, Lithography, Imaging systems, Digital micromirror devices, Data modeling, Convolution

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Proceedings Article | 18 July 2024 Paper

Comparison of reconstruction methods for optical projection tomography with sparse angle projections

Hantang Chen, Xu Ma, Jihui Wang

Proceedings Volume 13179, 1317918 (2024) https://doi.org/10.1117/12.3031592

KEYWORDS: Reconstruction algorithms, Image restoration, Biological samples, 3D projection, Imaging systems, Computer simulations, Projection systems, Optical tomography, Biological imaging, Tunable filters

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Proceedings Article | 13 June 2024 Paper

Compact colorful compressive spectral imager based on deep learning reconstruction

Jinshan Li, Xu Ma

Proceedings Volume 13180, 131807B (2024) https://doi.org/10.1117/12.3033570

KEYWORDS: Reconstruction algorithms, Image restoration, RGB color model, Deep learning, Tunable filters, Modulation, Imaging systems, Image compression, Optical filters, Education and training

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SPIE Journal Paper | 26 October 2023

Selection approach of critical patterns for calibrating the physical resist model based on spectrum coverage

Jiashuo Wang, Lisong Dong, Xiaojing Su, Yajuan Su, Xu Ma, Yayi Wei

JM3, Vol. 22, Issue 04, 043201, (October 2023) https://doi.org/10.1117/12.10.1117/1.JMM.22.4.043201

KEYWORDS: Calibration, Matrices, Amplifiers, Photoresist processing, Light sources and illumination, Diffraction, Semiconducting wafers, Lithography, Quenching, Process modeling

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BackgroundThe resist model is a significant element of computational lithography. Accurate calibration of the resist model is essential to obtain predictive results that closely match the exposure results on wafers. The trade-off between model accuracy and runtime is a challenging task. The selection of critical patterns used in model calibration affects these two metrics directly.AimHaving a dependable method for choosing critical patterns during resist model calibration is essential for lithography engineers with diverse professional backgrounds. Equally important is the ability to decrease runtime while maintaining the precision of calibrated resist models. To address these concerns, we present an approach for selecting critical patterns utilized in resist model calibration.ApproachSince the spectrum carries pattern information, critical patterns are selected based on the coverage of the spectrum in the frequency domain. The spectrum coverage (SCO) of each pattern in the entire test pattern set is calculated according to the frequency and amplitude of all the spectra. Combinations of critical patterns are selected based on their SCO values. The optimal combinations include the most types of spectra and aim to adapt the calibrated model for broad applicability, encompassing both universal and unique patterns.ResultsThe model verification results are compared with the experience-based methods, which select critical patterns based on the pitch-to-CD ratio. The accuracy and effectiveness of the proposed methodology have been demonstrated through experimental results. Compared with experience-based methods that only have dense and isolated critical patterns, our proposed method has a 9.8% increase in accuracy and a 35% decrease in runtime. Even compared with experience-based methods that include forbidden pitches, our method still achieves a 6.4% increase in accuracy without increasing runtime.ConclusionsIn summary, the suggested approach for selecting critical patterns based on SCO surpasses the experience-based methods in terms of both accuracy and efficiency. It can significantly shorten the modeling cycle of resists.

SPIE Journal Paper | 7 September 2023

Classification method of lithographic layout patterns based on graph convolutional network with graph attention mechanism

Junbi Zhang, Xu Ma, Shengen Zhang

JM3, Vol. 22, Issue 03, 034202, (September 2023) https://doi.org/10.1117/12.10.1117/1.JMM.22.3.034202

KEYWORDS: Matrices, Image classification, Lithography, Education and training, Convolution, Machine learning, Signal processing, Deep learning, Mathematical optimization, Data modeling

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SPIE Journal Paper | 29 June 2023

Instant coded X-ray computed tomography via nonlinear reconstruction

Qile Zhao, Xu Ma, Carlos Restrepo, Tianyi Mao, Tong Zhang, Wenyi Ren, Gonzalo Arce

OE, Vol. 62, Issue 06, 068107, (June 2023) https://doi.org/10.1117/12.10.1117/1.OE.62.6.068107

KEYWORDS: Reconstruction algorithms, Image restoration, X-ray sources, Simulations, X-ray computed tomography, CT reconstruction, X-rays, Detector arrays, Optical engineering, Matrices

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SPIE Journal Paper | 15 May 2023

Optimization of chemically amplified resist formulation based on simple random sampling and kernel density estimation

Pengjie Kong, Lisong Dong, Xu Ma, Yayi Wei

JM3, Vol. 22, Issue 02, 024601, (May 2023) https://doi.org/10.1117/12.10.1117/1.JMM.22.2.024601

KEYWORDS: Photoresist materials, Chemically amplified resists, Sampling rates, Photoresist developing, Chemical analysis, Quenching, Absorption, Statistical analysis, Refractive index, Matrices

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Proceedings Article | 1 May 2023 Poster + Paper

Predicting the critical features of the chemically-amplified resist profile based on machine learning

Pengjie Kong, Lisong Dong, Xu Ma, Yayi Wei

Proceedings Volume 12498, 124981U (2023) https://doi.org/10.1117/12.2658664

KEYWORDS: Photoresist materials, Neural networks, Machine learning, Artificial neural networks, Cadmium sulfide, Photoresist developing, Chemically amplified resists, Education and training, Computer simulations, Optical proximity correction

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Proceedings Article | 28 April 2023 Poster + Paper

Thick-mask model based on multi-channel U-Net for EUV lithography

Chengzhen Yu, Xu Ma

Proceedings Volume 12495, 1249525 (2023) https://doi.org/10.1117/12.2657246

KEYWORDS: Diffraction, Matrices, Extreme ultraviolet lithography, Education and training, Ultraviolet radiation, Convolution, Light sources and illumination, 3D mask effects, Extreme ultraviolet, Electric fields

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SPIE Journal Paper | 20 December 2022

Mitigating the forbidden pitch of extreme ultraviolet lithography using mask optimization based on genetic algorithm

Ling Ma, Lisong Dong, Taian Fan, Xu Ma, Yayi Wei

JM3, Vol. 21, Issue 04, 043204, (December 2022) https://doi.org/10.1117/12.10.1117/1.JMM.21.4.043204

KEYWORDS: Source mask optimization, Extreme ultraviolet lithography, Diffraction, Nanoimprint lithography, Lithography, Genetic algorithms, Mathematical optimization, Extreme ultraviolet, Lithographic illumination, Incident light

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Proceedings Article | 22 February 2021 Poster + Paper

Fast optical proximity correction based on graph convolution network

Shengen Zhang, Xu Ma, Junbi Zhang, Rui Chen, Yihua Pan, ChengZhen Yu, Lisong Dong, Yayi Wei, Gonzalo Arce

Proceedings Volume 11613, 116130V (2021) https://doi.org/10.1117/12.2583773

KEYWORDS: Optical proximity correction, Signal processing, Convolution, Imaging systems, Systems modeling, Photomasks, Optimization (mathematics), Machine learning, Lithography, Integrated circuits

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Proceedings Article | 22 February 2021 Poster + Paper

Lithography layout classification based on graph convolution network

Junbi Zhang, Xu Ma, Shengen Zhang, Xianqiang Zheng, Rui Chen, Yihua Pan, Lisong Dong, Yayi Wei, Gonzalo Arce

Proceedings Volume 11613, 116130U (2021) https://doi.org/10.1117/12.2583558

KEYWORDS: Lithography, Convolution, Signal processing, Network architectures, Image classification, Transform theory, Source mask optimization, Machine learning, Integrated circuits, Fourier transforms

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Proceedings Article | 23 April 2020 Presentation + Paper

Coded aperture optimization in x-ray tomosynthesis via sparse principal component analysis

Tianyi Mao, Xu Ma, Angela Cuadros, Gonzalo Arce

Proceedings Volume 11404, 114040E (2020) https://doi.org/10.1117/12.2556795

KEYWORDS: Coded apertures, X-rays, X-ray sources, Principal component analysis, 3D image processing, Optimization (mathematics), Reconstruction algorithms, X-ray imaging

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Proceedings Article | 23 March 2020 Paper

Comparison of different lithographic source optimization methods based on compressive sensing

Zhiqiang Wang, Xu Ma, Rui Chen, Gonzalo Arce, Lisong Dong, Hans-Juergen Stock, Yayi Wei

Proceedings Volume 11327, 1132716 (2020) https://doi.org/10.1117/12.2551037

KEYWORDS: Source mask optimization, Reconstruction algorithms, Lithography, Computer simulations, Imaging systems, Lithographic illumination, Compressed sensing, Computational lithography, Optical lithography

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Proceedings Article | 23 March 2020 Paper

Compressive sensing method for EUV source optimization using different bases

Jiaxin Lin, Lisong Dong, Taian Fan, Xu Ma, Rui Chen, Xiaoran Zhang, Hans-Juergen Stock, Yayi Wei

Proceedings Volume 11323, 113232J (2020) https://doi.org/10.1117/12.2551897

KEYWORDS: Extreme ultraviolet, Compressed sensing, Extreme ultraviolet lithography, Computer simulations

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Proceedings Article | 10 September 2019 Paper

Compressive x-ray material decomposition using structured illumination

Angela Cuadros, Xu Ma, Gonzalo Arce

Proceedings Volume 11113, 111131H (2019) https://doi.org/10.1117/12.2526843

KEYWORDS: X-rays, Sensors, Optical filters, Signal attenuation, X-ray imaging, X-ray technology, Reconstruction algorithms, Coded apertures, Linear filtering, X-ray sources, Spectral data processing

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Proceedings Article | 17 September 2018 Paper

Optimization of coded aperture in compressive x-ray tomography

Tianyi Mao, Angela Cuadros, Xu Ma, Weiji He, Qian Chen, Gonzalo Arce

Proceedings Volume 10752, 107521S (2018) https://doi.org/10.1117/12.2319429

KEYWORDS: Coded apertures, X-rays, Tomography, Sensors, X-ray sources, Reconstruction algorithms, Optimization (mathematics), Computer simulations, Computed tomography, Signal processing

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Proceedings Article | 14 May 2018 Presentation + Paper

Snapshot compressive spectral imaging based on adaptive coded apertures

Xu Ma, Hao Zhang, Xiao Ma, Gonzalo Arce, Tingfa Xu, Tianyi Mao

Proceedings Volume 10658, 1065803 (2018) https://doi.org/10.1117/12.2309809

KEYWORDS: Coded apertures, Staring arrays, Imaging systems, Hyperspectral imaging, Signal to noise ratio, Cameras, Reconstruction algorithms, Sensors, Imaging spectroscopy

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SPIE Journal Paper | 5 May 2015

Gradient-based joint source polarization mask optimization for optical lithography

Xu Ma, Lisong Dong, Chunying Han, Jie Gao, Yanqiu Li, Gonzalo Arce

JM3, Vol. 14, Issue 02, 023504, (May 2015) https://doi.org/10.1117/12.10.1117/1.JMM.14.2.023504

KEYWORDS: Polarization, Source mask optimization, Photomasks, Optical proximity correction, Optical lithography, Semiconducting wafers, Computer simulations, Optimization (mathematics), Detection and tracking algorithms, Manufacturing

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Proceedings Article | 18 March 2015 Paper

A fast and flexible library-based thick-mask near-field calculation method

Xu Ma, Jie Gao, Xuanbo Chen, Lisong Dong, Yanqiu Li

Proceedings Volume 9426, 94261G (2015) https://doi.org/10.1117/12.2085010

KEYWORDS: Diffraction, Matrices, Photomasks, Near field, Finite-difference time-domain method, Computer simulations, Calibration, Semiconducting wafers, Lithography, Image processing

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SPIE Journal Paper | 21 November 2014 Open Access

Parametric source-mask-numerical aperture co-optimization for immersion lithography

Xuejia Guo, Yanqiu Li, Lisong Dong, Lihui Liu, Xu Ma, Chunying Han

JM3, Vol. 13, Issue 04, 043013, (November 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.4.043013

KEYWORDS: Photomasks, Source mask optimization, Artificial intelligence, Immersion lithography, Lithography, Optical fiber cables, Image processing, Binary data, Imaging systems, Resolution enhancement technologies

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SPIE Journal Paper | 3 November 2014 Open Access

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

Xu Ma, Bingliang Wu, Zhiyang Song, Shangliang Jiang, Yanqiu Li

JM3, Vol. 13, Issue 04, 043007, (November 2014) https://doi.org/10.1117/12.10.1117/1.JMM.13.4.043007

KEYWORDS: Optical proximity correction, Metals, Photomasks, Algorithm development, Semiconducting wafers, Binary data, Image processing, Resolution enhancement technologies, Optical lithography, SRAF

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Proceedings Article | 31 March 2014 Paper

Efficient source polarization optimization for robust optical lithography

Xu Ma, Jie Gao, Chunying Han, Yanqiu Li, Lisong Dong, Lihui Liu

Proceedings Volume 9052, 90520T (2014) https://doi.org/10.1117/12.2045724

KEYWORDS: Polarization, Optimization (mathematics), Photomasks, Neptunium, Optical lithography, Computer simulations, Source mask optimization, Lithography, Imaging systems, Modulation

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SPIE Journal Paper | 15 November 2012

Vectorial mask optimization methods for robust optical lithography

Xu Ma, Yanqiu Li, Xuejia Guo, Lisong Dong

JM3, Vol. 11, Issue 4, 043008, (November 2012) https://doi.org/10.1117/12.10.1117/1.JMM.11.4.043008

KEYWORDS: Optical proximity correction, Photomasks, Optical lithography, Source mask optimization, Semiconducting wafers, Systems modeling, Imaging systems, Polarization, Wavefront aberrations, Detection and tracking algorithms

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Proceedings Article | 13 March 2012 Paper

Gradient-based resolution enhancement optimization methods based on vector imaging model

Xu Ma, Yanqiu Li, Lisong Dong

Proceedings Volume 8326, 83262B (2012) https://doi.org/10.1117/12.916144

KEYWORDS: Imaging systems, Photomasks, Systems modeling, Optical proximity correction, Optimization (mathematics), Semiconducting wafers, Resolution enhancement technologies, Source mask optimization, Wavelets, Image processing

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Proceedings Article | 13 March 2012 Paper

Robust resolution enhancement optimization methods to process variations based on vector imaging model

Xu Ma, Yanqiu Li, Xuejia Guo, Lisong Dong

Proceedings Volume 8326, 83262A (2012) https://doi.org/10.1117/12.916147

KEYWORDS: Optical proximity correction, Photomasks, Imaging systems, Optical lithography, Optimization (mathematics), Detection and tracking algorithms, Semiconducting wafers, Resolution enhancement technologies, Algorithm development, Systems modeling

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SPIE Journal Paper | 1 April 2011

Resolution enhancement optimization methods in optical lithography with improved manufacturability

Xu Ma, Yanqiu Li

JM3, Vol. 10, Issue 02, 023009, (April 2011) https://doi.org/10.1117/12.10.1117/1.3590252

KEYWORDS: Photomasks, Manufacturing, Optical lithography, Resolution enhancement technologies, Optics manufacturing, Optimization (mathematics), Imaging systems, Optical proximity correction, Lithium, Image resolution

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Proceedings Article | 23 March 2011 Paper

A recursive cost-based approach to fracturing

Shangliang Jiang, Xu Ma, Avideh Zakhor

Proceedings Volume 7973, 79732P (2011) https://doi.org/10.1117/12.879583

KEYWORDS: Optical proximity correction, Photomasks, Metals, Algorithm development, Critical dimension metrology, Vestigial sideband modulation, Computer programming, Optical lithography, Beam shaping, Computer simulations

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Proceedings Article | 23 March 2011 Paper

A cost-driven fracture heuristics to minimize external sliver length

Xu Ma, Shangliang Jiang, Avideh Zakhor

Proceedings Volume 7973, 79732O (2011) https://doi.org/10.1117/12.879276

KEYWORDS: Photomasks, Critical dimension metrology, Computer programming, Optical proximity correction, Resolution enhancement technologies, Vestigial sideband modulation, SRAF, Optical lithography, Beam shaping, Transistors

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Proceedings Article | 4 March 2010 Paper

Binary mask optimization for forward lithography based on boundary layer model in coherent systems

Xu Ma, Gonzalo Arce

Proceedings Volume 7640, 76402N (2010) https://doi.org/10.1117/12.836858

KEYWORDS: Photomasks, Binary data, Systems modeling, Optical proximity correction, Source mask optimization, Optical lithography, Lithography, Near field, Algorithm development, Model-based design

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Proceedings Article | 16 March 2009 Paper

PSM design for inverse lithography with partially coherent illumination

Xu Ma, Gonzalo Arce

Proceedings Volume 7274, 727437 (2009) https://doi.org/10.1117/12.811762

KEYWORDS: Photomasks, Photoresist materials, Lithographic illumination, Lithography, Binary data, Systems modeling, Optical lithography, Semiconducting wafers, Optimization (mathematics), Phase shifts

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Proceedings Article | 4 December 2008 Paper

Binary mask optimization for inverse lithography with partially coherent illumination

Xu Ma, Gonzalo Arce

Proceedings Volume 7140, 71401A (2008) https://doi.org/10.1117/12.803408

KEYWORDS: Photomasks, Lithographic illumination, Binary data, Lithography, Optimization (mathematics), Wavelets, Systems modeling, Optical proximity correction, Source mask optimization, Algorithm development

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Proceedings Article | 27 March 2007 Paper

Generalized inverse lithography methods for phase-shifting mask design

Xu Ma, Gonzalo Arce

Proceedings Volume 6520, 65200U (2007) https://doi.org/10.1117/12.711559

KEYWORDS: Photomasks, Wavelets, Lithography, Binary data, Resolution enhancement technologies, Image processing, Algorithm development, Photoresist materials, Phase shifts, Phase shifting

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Showing 5 of 34 publications

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