Dr. Chen Li Profile

Dr. Chen Li

at ICRD

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Author

Publications (3)

SPIE Journal Paper | 29 November 2022

Can optical proximity correction solution be learned? The learning limit and a general learning framework

Xuelong Shi, Yan Yan, Chen Li, Mingyang Xia, Bingyang Pan, Ying Gao, Wei Yuan

JM3, Vol. 21, Issue 04, 043203, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMM.21.4.043203

KEYWORDS: Optical proximity correction, Machine learning, Data modeling, Statistical modeling, Photomasks, Education and training, SRAF, Performance modeling, Deep convolutional neural networks, Visual process modeling

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SPIE Journal Paper | 29 September 2021

Machine learning virtual SEM metrology and SEM-based OPC model methodology

Yan Yan, Xuelong Shi, Tao Zhou, Bowen Xu, Chen Li, Wei Yuan, Ying Gao, Bingyang Pan, Xuling Diao, Shoumian Chen, Yuhang Zhao

JM3, Vol. 20, Issue 04, 041204, (September 2021) https://doi.org/10.1117/12.10.1117/1.JMM.20.4.041204

KEYWORDS: Scanning electron microscopy, Data modeling, Metrology, Optical proximity correction, Machine learning, 3D modeling, Performance modeling, Image processing, Statistical modeling, Convolution

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Background: E-beam metrologies, both critical dimension scanning electron microscope metrology and defect scan metrology, have been playing a very critical role in gating patterning quality. SEM images can provide rich visual information for engineers to do qualitative and quantitative analyses. However, the low e-beam metrology tool throughput makes it impossible to obtain SEM images for larger area. Monte Carlo-based SEM image simulations or other SEM image simulations require postlithography or postetch pattern three-dimensional structures as prerequisite, and the simulation speed is not sufficiently fast for full chip implementation. Aim: We aim to develop machine learning SEM models with sufficient accuracy and speed for full chip application in semiconductor manufacturing environment. Approach: We have proposed a virtual SEM metrology solution based on U-Net neural network with physics-based feature maps as model input. With information in aerial image space encoded properly, SEM images of both postlithography and postetch can be predicted accurately enough for practical applications using our proposed virtual SEM metrology models. Equipped with GPUs, the machine learning-based SEM image models are fast enough to make it possible to realize full chip SEM generation from post-OPC data. Results: Our machine learning SEM image models can predict SEM images with normalized cross correlation around 0.95 in reference to ground truth SEM images, each SEM image (512 × 512 in size) takes about 800 ms using single CPU, and the speed can be accelerated to about 10 ms with single GPU. Conclusions: Using U-Net structure and physics-based feature maps as model inputs, machine learning-based SEM image models can be developed. The models are sufficiently accurate and fast to find their applications in semiconductor manufacturing, and they can be used as independent model for OPC data verification or generate SEM images as reference for SEM defect scan metrology.

Proceedings Article | 23 March 2020 Paper

Physics based feature vector design: a critical step towards machine learning based inverse lithography

Xuelong Shi, Yuhang Zhao, Shoumian Chen, Chen Li

Proceedings Volume 11327, 113270A (2020) https://doi.org/10.1117/12.2551571

KEYWORDS: Machine learning, Lithography, Computational lithography, Neural networks, Photomasks, SRAF

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