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Related Experiment Video

Updated: Feb 20, 2026

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Fast lithographic source optimization using a batch-processing sequential least square estimator.

Xu Ma, Haijun Lin, Guoli Jiao

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    |October 20, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a fast source optimization (SO) method for lithography, improving imaging performance for difficult-to-print layout areas called hotspots. The new approach iteratively updates source patterns, avoiding recalculations for new hotspot data and accelerating the process.

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    Area of Science:

    • * Lithography and semiconductor manufacturing.
    • * Computational imaging and optical physics.

    Background:

    • * Full-chip layouts in lithography present numerous 'hotspots'—critical areas with challenging geometric characteristics that are difficult to print accurately.
    • * Existing source optimization (SO) methods often require recalculating optimal sources when new hotspot data emerges, leading to inefficiencies.
    • * Current SO approaches aggregate data from all hotspots before optimization, which can be computationally intensive and inflexible.

    Purpose of the Study:

    • * To develop a novel, accelerated source optimization method for lithography systems.
    • * To enhance the imaging performance across multiple, diverse hotspots on a full-chip layout.
    • * To create a flexible SO approach that accommodates new hotspot data without full recalculation.

    Main Methods:

    • * Development of a batch-processing sequential least square estimator.
    • * Iterative modification of the source pattern using ongoing hotspot data.
    • * Sequential updating of optimized sources to accommodate new or different hotspot characteristics.

    Main Results:

    • * Significant acceleration of the source optimization procedure.
    • * Demonstrated improvement in imaging performance for multiple hotspots.
    • * Validation of the method's efficiency in handling diverse hotspot geometries.
    • * The proposed method avoids redundant computations when new hotspot data becomes available.

    Conclusions:

    • * The developed batch-processing sequential least square estimator offers a fast and efficient SO method for lithography.
    • * The iterative approach successfully improves imaging performance for multiple hotspots.
    • * This technique enhances computational efficiency by allowing incremental updates to source patterns.
    • * The method provides a flexible solution for optimizing complex full-chip layouts with varying hotspot challenges.