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Hybrid source mask optimization for robust 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, China.

Applied Optics
|July 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid source and mask optimization (HSMO) algorithm for advanced immersion lithography. The new method enhances process robustness against defocus and dose variations, improving manufacturing precision.

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Area of Science:

  • Semiconductor manufacturing
  • Optical lithography
  • Computational imaging

Background:

  • Critical dimension shrinkage necessitates advanced resolution enhancement techniques.
  • Scalar imaging models are insufficient for modern hyper-NA immersion lithography.
  • Process robustness to defocus and dose variations is crucial for yield.

Purpose of the Study:

  • Develop a robust hybrid source and mask optimization (HSMO) algorithm.
  • Improve the process robustness of 193 nm argon fluoride immersion lithography systems.
  • Address limitations of scalar models in hyper-NA systems.

Main Methods:

  • Utilized a vector imaging model for accuracy in hyper-NA systems.
  • Implemented a two-step optimization: individual source optimization followed by simultaneous SMO.
  • Employed the conjugate gradient method for pixel updates.
  • Incorporated source regularization and postprocessing for manufacturability.

Main Results:

  • The HSMO algorithm effectively improves robustness to defocus and dose variations.
  • Achieved larger process windows compared to mask optimization alone.
  • Extended depth of focus and exposure latitude for 45 nm immersion lithography.
  • Demonstrated superior performance over scalar-based SMO approaches.

Conclusions:

  • The proposed HSMO algorithm offers a significant advancement for immersion lithography.
  • HSMO enhances process windows, leading to improved manufacturing yield.
  • Vector imaging models are essential for optimizing next-generation lithography systems.