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

doi:10.1117/12.2516236

17 May 2019 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

Author Affiliations +

Proceedings Volume 10961, Optical Microlithography XXXII; 109610N (2019) https://doi.org/10.1117/12.2516236
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

The semiconductor manufacturing roadmap which generally follows Moore’s law requires smaller and smaller EPE (Edge Placement Error), and this places stricter requirements on OPC model accuracy, which is mainly limited by metrology errors, pattern coverage and model form. Current metrology errors are mainly related to SEM image noise and measurement difficulty in complex 2D patterns. And traditional model form improvement by adding empirical terms for PEB (Post Exposure Bake), NTD (Negative Tone Development) and PRS (Physical Resist Shrinkage) effects still cannot meet the accuracy spec because other physical and chemical effects are uncaptured. Fitting these effects also requires comprehensive pattern coverage during model calibration. Solely improving model form may overfit the metrology error, which is risky, while solely improving metrology ignores existing model errors: both factors are troublesome for OPC. In this paper, a new metrology (MXP, naming for Metrology of Extreme Performance) and deep learning (Newron, naming for a Deep Convolutional Neural Network model form) integrated solution is proposed, where MXP decreases the metrology errors and provides good pattern coverage with high-volume reliable CD and EP (Edge Placement) gauges, and Newron captures remaining complex physical and chemical effects embedded in high-volume gauges beyond the traditional model. This solution shows overall ~30% prediction accuracy improvement compared to baseline metrology and FEM+ (Focus Exposure Matrix) model flow in N14 NTD process, predicts SEM shape of critical weak points more accurately.

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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, and Jun Lang "Metrology and deep learning integrated solution to drive OPC model accuracy improvement", Proc. SPIE 10961, Optical Microlithography XXXII, 109610N (17 May 2019); https://doi.org/10.1117/12.2516236

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