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Related Concept Videos

Deconvolution01:20

Deconvolution

484
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
484

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Micromirror array allocation algorithm based on deconvolution.

Zhifan Liu, Chaoxing Yang, Yang Bu

    Applied Optics
    |May 14, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A new deconvolution-based algorithm rapidly generates precise freeform illumination sources using micromirror arrays (MMA). This method significantly enhances computational speed and minimizes process window loss in advanced lithography.

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    Area of Science:

    • Semiconductor Manufacturing
    • Optical Lithography
    • Computational Optics

    Background:

    • Freeform illumination is critical for advanced semiconductor manufacturing at 28 nm nodes and beyond.
    • Micromirror arrays (MMAs) offer programmability for lithography freeform illumination systems.
    • Efficient MMA allocation algorithms are essential for precise illumination source generation.

    Purpose of the Study:

    • To propose a novel MMA allocation method based on deconvolution for generating target freeform illumination sources.
    • To improve the computational speed and precision of freeform illumination source generation.
    • To reduce process window loss in lithography.

    Main Methods:

    • Developed an MMA allocation method utilizing deconvolution and quantization processes.
    • Directly generated the target freeform illumination source without iterative optimization.
    • Employed numerical simulations to validate the method's performance.

    Main Results:

    • Achieved a difference of less than 0.2% between the target source and the MMA source.
    • Demonstrated process window loss of less than 0.5% for the generated MMA source.
    • Reduced runtime to less than 0.05 s, a 1463-fold improvement over traditional methods.

    Conclusions:

    • The proposed deconvolution-based MMA allocation method is significantly faster and more precise than traditional approaches.
    • This technique enables efficient and accurate generation of freeform illumination sources for advanced lithography.
    • The method offers substantial improvements in computational speed and process window preservation.