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Pixelated source and mask optimization for immersion lithography.

Xu Ma1, Chunying Han, Yanqiu Li

  • 1Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|March 5, 2013
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Summary
This summary is machine-generated.

This study introduces advanced pixelated gradient-based source and mask optimization (SMO) algorithms using a vector imaging model for hyper-numerical aperture (hyper-NA) immersion lithography. A novel hybrid SMO approach achieves superior performance for next-generation nanolithography.

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Area of Science:

  • Semiconductor manufacturing
  • Optical lithography
  • Nanotechnology

Background:

  • Immersion lithography with hyper-numerical aperture (hyper-NA) is crucial for advanced nanolithography (45 nm nodes and beyond).
  • Source and Mask Optimization (SMO) enhances immersion lithography performance.
  • Existing pixelated SMO methods based on scalar models are inadequate for hyper-NA systems.

Purpose of the Study:

  • Develop accurate pixelated gradient-based SMO algorithms for hyper-NA immersion lithography using a vector imaging model.
  • Formulate simultaneous (SISMO) and sequential (SESMO) SMO frameworks.
  • Compare the performance of various SMO strategies.

Main Methods:

  • Utilized an integrative and analytic vector imaging model for hyper-NA lithography.
  • Developed gradient-based algorithms for joint source and mask optimization.
  • Implemented and compared individual source optimization (SO), individual mask optimization (MO), SISMO, and SESMO frameworks.

Main Results:

  • The vector imaging model provides accurate predictions for hyper-NA lithography.
  • SISMO and SESMO frameworks were successfully formulated and implemented.
  • Performance comparison revealed varying efficiencies of SO, MO, SISMO, and SESMO.

Conclusions:

  • Pixelated gradient-based SMO using a vector imaging model is essential for hyper-NA immersion lithography.
  • A proposed hybrid SMO (HSMO) approach integrates SO, SISMO, and MO for optimal results.
  • HSMO demonstrates superior performance, advancing nanolithography capabilities.