• Laser & Optoelectronics Progress
  • Vol. 59, Issue 9, 0922007 (2022)
Guodong Chen1、2, Zinan Zhang1、2, Sikun Li1、2, and Xiangzhao Wang1、2、*
Author Affiliations
  • 1Laboratory of Information Optics and Opto-Electronic Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 2Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.3788/LOP202259.0922007 Cite this Article Set citation alerts
    Guodong Chen, Zinan Zhang, Sikun Li, Xiangzhao Wang. Study on Deep Ultraviolet Computational Lithography Techniques[J]. Laser & Optoelectronics Progress, 2022, 59(9): 0922007 Copy Citation Text show less

    Abstract

    Lithography tool is the core equipment for the ultra-large-scale integrated circuit (ULSI) manufacturing. Deep ultraviolet (DUV) lithography tool is the mainstream lithographic equipment in the advanced technology node of chip manufacturing. The imaging quality of lithography tool, which has a direct impact on the performance metrics, is the premise that the lithography tool can work properly. Computational lithography technique is a vital way to improve the lithographic imaging quality when the software and hardware of lithography tool remain unchanged. It optimizes the illumination source, the mask pattern, and the process parameters using mathematical models and algorithms. With the help of computational lithography, the target pattern can be transferred onto the wafer with high imaging fidelity. Lithographic imaging model is the basis of computational lithography technique. The continuous increase of imaging model’s simulation accuracy and speed supports the development of computational lithography technique. Combining the research work of our group, the development of lithographic imaging model is reviewed. Then the research progresses of three main computational lithography techniques, including optical proximity effect correction (OPC), source mask optimization (SMO), and inverse lithography technology (ILT), are summarized in this paper.
    Guodong Chen, Zinan Zhang, Sikun Li, Xiangzhao Wang. Study on Deep Ultraviolet Computational Lithography Techniques[J]. Laser & Optoelectronics Progress, 2022, 59(9): 0922007
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