Mr. Yuhang Zhao Profile

Yuhang Zhao

at ICRD

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Publications (5)

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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Proceedings Article | 23 March 2020 Presentation + Paper

Accurate etch modeling with massive metrology and deep-learning technology

Yifei Lu, Yuhang Zhao, Ming Li, Wei Yuan, Xiang Peng, Hongmei Hu, Shuxin Yao, Zhunhua Liu, Yu Tian, Ying Gao, Bingyang Pan, Weijun Wang, Chunyan Yi, Jinze Wang, Qian Xie, Xichen Sheng, Ying-chen Wu, Guanyong Yan, Yanjun Xiao, Liang Liu, Liang Ji, Qian Zhao, Yongfa Fan, Yiqiong Zhao, Mu Feng, Yueliang Yao, Terrance Yang, Jun Lang

Proceedings Volume 11327, 113270B (2020) https://doi.org/10.1117/12.2552001

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

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The semiconductor design node shrinking requires tighter edge placement errors (EPE) budget. OPC error, as one major contributor of EPE budget, need to be reduced with better OPC model accuracy. In addition, the CD (Critical Dimension) shrinkage in advanced node heavily relies on the etch process. Therefore AEI (After Etch Inspection) metrology and modeling are important to provide accurate pattern correction and optimization. For nodes under 14nm, the etch bias (i.e. the bias between ADI (After Development Inspection) CD and AEI CD) could be -10 nm ~ -50 nm, with a strong loading and aspect-ratio dependency. Etch behavior in advanced node is very complicated and brings challenges to conventional rule based OPC correction. Therefore, accurate etch modeling becomes more and more important to make precise prediction of final complex shapes on wafer for OPC correction. In order to ensure the accuracy of etch modeling, high quality metrology is necessary to reduce random error and systematic measurement error. Moreover, CD gauges alone are not sufficient to capture all the effects of the etch process on different patterns. Edge placement (EP) gauges that accurately describe the contour shapes at various key positions are needed. In this work we used the AEI SEM images obtained from traditional CD-SEM flow, processed with ASML’s MXP (Metrology for eXtreme Performance) tool, and used the extracted CD gauges and massive EP gauges to train a deeplearning Newron Etch model. In the approach, MXP reduced the AEI metrology random errors and shape fitting measurement error and provides better pattern coverage with massive reliable CD and EP gauges, Newron Etch captures complex and unknown physical and chemical effects learned from wafer data. Results shows that MXP successfully extracted stable contour from AEI SEM for various pattern types. Three etch models are calibrated and compared: CD based EEB model (Effective Etch Bias), CD+EP based EEB model, and CD+EP based Newron etch model. CD based EEB model captures the major trend of the etch process. Including EP gauges helps EEB model with about 10% RMS reduction on prediction. Integration of MXP (CD+EP) and Newron Etch model gains about 45% prediction RMS reduction compared to baseline model. The good prediction of Newron Etch is also verified from wafer SEM overlay on complex-shape patterns. This result validates the effectiveness of ASML’s solution of deep learning etch model integration with MXP AEI’s massive wafer data extraction from etch process, and will help to provide accurate and reliable etch modeling for advanced node etch OPC correction in semiconductor manufacturing.

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 Paper

Optimal feature vector design for computational lithography

Xuelong Shi, YuHang Zhao, Shoumian Cheng, Ming Li, Wei Yuan, Leon Yao, Wenhao Zhao, Yanjun Xiao, Xiaohui Kang, Angmar Li

Proceedings Volume 10961, 109610O (2019) https://doi.org/10.1117/12.2515446

KEYWORDS: Computational lithography, Machine learning, Neural networks, Data modeling, Optical proximity correction, Lithography, Feature extraction, Convolution, Physics, Process modeling

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