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Related Experiment Videos

Lithographic source optimization based on adaptive projection compressive sensing.

Xu Ma, Dongxiang Shi, Zhiqiang Wang

    Optics Express
    |April 7, 2017
    PubMed
    Summary
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    This study introduces data-adaptive compressive sensing (CS) for lithography source optimization (SO). Adaptive projections, derived from layout data, outperform random projections for improved image fidelity.

    Area of Science:

    • Semiconductor manufacturing
    • Computational imaging
    • Data science

    Background:

    • Source optimization (SO) in lithography is crucial for improving image fidelity.
    • Traditional methods may not fully leverage target layout information for efficiency.
    • Compressive sensing (CS) offers a framework for efficient data acquisition and reconstruction.

    Purpose of the Study:

    • To develop data-adaptive compressive sensing (CS) methods for efficient source optimization (SO) in lithography.
    • To utilize a-priori knowledge of target layout patterns for enhanced SO performance.
    • To investigate the effectiveness of adaptive projections and sparse representation bases.

    Main Methods:

    • Selection of monitoring pixels using blue noise patterns from target layouts.

    Related Experiment Videos

  • Formulation of SO as an under-determined linear problem based on monitoring pixels.
  • Design of adaptive projections using a-priori layout knowledge and nonlinear thresholding.
  • Comparison of adaptive projections against random projections.
  • Evaluation of different sparse representation bases (DCT, wavelet).

    • Main Results:

    • Adaptive projections demonstrate superior SO performance compared to random projections.
    • The use of a-priori layout knowledge in adaptive projections significantly enhances SO.
    • Discrete Cosine Transform (DCT), spatial, and Haar wavelet bases are effective for source representation in this context.

    Conclusions:

    • Data-adaptive CS with layout-informed projections is a highly effective strategy for lithography SO.
    • The proposed method improves image fidelity while reducing computational complexity.
    • Sparse representation choice impacts SO performance, with DCT and wavelets showing promise.