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  • Received: Apr. 24, 2019

    Accepted: Aug. 6, 2019

    Posted: Dec. 3, 2019

    Published Online: Dec. 3, 2019

    The Author Email: Yijiang Shen (yjshen@gdut.edu.cn)

    DOI: 10.3788/COL201917.121102

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    Yijiang Shen, Fei Peng, Xiaoyan Huang, Zhenrong Zhang. Adaptive gradient-based source and mask co-optimization with process awareness[J]. Chinese Optics Letters, 2019, 17(12): 121102

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Chinese Optics Letters, Vol. 17, Issue 12, 121102 (2019)

Adaptive gradient-based source and mask co-optimization with process awareness

Yijiang Shen1,*, Fei Peng1, Xiaoyan Huang1, and Zhenrong Zhang2

Author Affiliations

  • 1School of Automation, Guangdong University of Technology, Mega Education Center South, Guangzhou 510006, China
  • 2Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China

Abstract

We develop a source and mask co-optimization framework incorporating the minimization of edge placement error (EPE) and process variability band (PV Band) into the cost function to compensate simultaneously for the image distortion and the increasingly pronounced lithographic process conditions. Explicit differentiable functions of the EPE and the PV Band are presented, and adaptive gradient methods are applied to break symmetry to escape suboptimal local minima. Dependence on the initial mask conditions is also investigated. Simulation results demonstrate the efficacy of the proposed source and mask optimization approach in pattern fidelity improvement, process robustness enhancement, and almost unaffected performance with random initial masks.