Dr. Jinze Wang Profile

Dr. Jinze Wang

at Brion Technologies (Shenzhen) Co Ltd

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Publications (6)

Proceedings Article | 28 April 2023 Presentation + Paper

A holistic approach to model-based stochastic-aware computational lithography

ChangAn Wang, Yongfa Fan, Mu Feng, Qian Xie, Jazer Wang, Chris Kaplan, Michael Crouse, Xiaoyang Li, Stephen Hsu, Peigen Cao, Yi-Hsing Peng, Stephen Chang, Jun Ye, Youping Zhang, Bin Cheng, Ken Yang, Leiwu Zheng, Jen-Shiang Wang, Austin Peng, Li-Hao Yeh, Cuiping Zhang, Rafael Howell, Alexander Tan, Yiqiong Zhao, Jun Lang, Xiaolong Zhang

Proceedings Volume 12494, 124940B (2023) https://doi.org/10.1117/12.2658508

KEYWORDS: Stochastic processes, Data modeling, Optical proximity correction, Line width roughness, Source mask optimization, Computational lithography, Semiconducting wafers, Modeling, Performance modeling, Photons

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Proceedings Article | 25 May 2022 Presentation + Paper

OPC accuracy improvement through deep-learning based etch model

Weina Shi, Liang Zhu, Yuqian Chen, Jiao Huang, Jinze Wang, Qian Xie, Bilun Zhang, Yunfei Xi, Wenhao Pan, Yuanxia Zheng, Yongfa Fan, Jin Cheng, Yu Zhao, Leiwu Zheng

Proceedings Volume 12056, 1205607 (2022) https://doi.org/10.1117/12.2613926

KEYWORDS: Etching, Process modeling, Data modeling, Performance modeling, Optical proximity correction, Semiconducting wafers, Scanning electron microscopy, Calibration, Model-based design, Metrology

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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 | 22 March 2018 Paper

Massive metrology using fast e-beam technology improves OPC model accuracy by >2x at faster turnaround time

Qian Zhao, Lei Wang, Jazer Wang, ChangAn Wang, Hong-Fei Shi, James Guerrero, Mu Feng, Qiang Zhang, Jiao Liang, Yunbo Guo, Chen Zhang, Tom Wallow, David Rio, Lester Wang, Alvin Wang, Jen-Shiang Wang, Keith Gronlund, Jun Lang, Kar Kit Koh, Dong Qing Zhang, Hongxin Zhang, Subramanian Krishnamurthy, Ray Fei, Chiawen Lin, Wei Fang, Fei Wang

Proceedings Volume 10585, 105852Q (2018) https://doi.org/10.1117/12.2299971

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

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Classical SEM metrology, CD-SEM, uses low data rate and extensive frame-averaging technique to achieve high-quality SEM imaging for high-precision metrology. The drawbacks include prolonged data collection time and larger photoresist shrinkage due to excess electron dosage. This paper will introduce a novel e-beam metrology system based on a high data rate, large probe current, and ultra-low noise electron optics design. At the same level of metrology precision, this high speed e-beam metrology system could significantly shorten data collection time and reduce electron dosage. In this work, the data collection speed is higher than 7,000 images per hr. Moreover, a novel large field of view (LFOV) capability at high resolution was enabled by an advanced electron deflection system design. The area coverage by LFOV is >100x larger than classical SEM. Superior metrology precision throughout the whole image has been achieved, and high quality metrology data could be extracted from full field. This new capability on metrology will further improve metrology data collection speed to support the need for large volume of metrology data from OPC model calibration of next generation technology. The shrinking EPE (Edge Placement Error) budget places more stringent requirement on OPC model accuracy, which is increasingly limited by metrology errors. In the current practice of metrology data collection and data processing to model calibration flow, CD-SEM throughput becomes a bottleneck that limits the amount of metrology measurements available for OPC model calibration, impacting pattern coverage and model accuracy especially for 2D pattern prediction. To address the trade-off in metrology sampling and model accuracy constrained by the cycle time requirement, this paper employs the high speed e-beam metrology system and a new computational software solution to take full advantage of the large volume data and significantly reduce both systematic and random metrology errors. The new computational software enables users to generate large quantity of highly accurate EP (Edge Placement) gauges and significantly improve design pattern coverage with up to 5X gain in model prediction accuracy on complex 2D patterns. Overall, this work showed >2x improvement in OPC model accuracy at a faster model turn-around time.

Proceedings Article | 24 March 2017 Presentation + Paper

Accurate characterization of 2D etch bias by capturing surrounding effects from resist and trench areas

Yongfa Fan, Leiwu Zheng, Mu Feng, Jinze Wang, Qiao Zhao, Jen-Shiang Wang, Rafael Howell, Keith Gronlund

Proceedings Volume 10147, 101470Z (2017) https://doi.org/10.1117/12.2258702

KEYWORDS: Etching, Optical lithography, Optical proximity correction, Calibration, Reactive ion etching, Plasma, Plasma etching

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The extension of optical lithography to 7 nm node and beyond relies heavily on multiple litho-etch patterning technologies. The etch processes in multiple patterning often require progressively large bias differences between litho and etch as the target features become smaller. Moreover, since this litho-etch bias has strong pattern dependency, it must be taken into consideration during the Optical Proximity Correction (OPC) processes. Traditionally, two approaches are used to compensate etch biases: rule-based retargeting and model-based retargeting. The rule-based approach has a turn-around-time advantage but now has challenges meeting the increasingly tighter critical dimension (CD) requirements using a reasonable etch-bias table, especially for complex 2D patterns. Alternatively, model-based retargeting can meet these CD requirements by capturing the etch process physics with high accuracy, including the etch bias variability that arises from both patterning proximity effects and etch chamber non-uniformity. In the past, empirical terms have been used to approximate the etch bias due to pattern proximity effects but sometimes empirical models are known to have compromised model accuracy so a physical based approach is desired. This paper’s work will address the etch bias variability due to patterning proximity effects by using a physical approach based simplified chemical kinetics. It starts from a well calibrated After-Development-Inspection (ADI) model and the subsequent etch model is based on the ADI model contour. By assuming that plasma chemical species in the trenches are maintained in an equilibrium state, the plasma species act on the edges to induce etch bias. Methods are developed to evaluate plasma collision probability on trench edges for random layouts. Furthermore, the impact of resist materials on etch bias are treated with Arrhenius equation or as a second order reaction. Equations governing plasma collision probabilities on trench edges as a function of time are derived. An etch bias model can be calibrated based on those equations. Experimental results have shown that this physical approach to model etch bias is a promising direction to applications for full-chip etch proximity corrections.

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