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Demand-driven dynamic control points insertion for high-fidelity and efficient curvilinear optical proximity

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    A new dynamic framework improves curvilinear optical proximity correction by intelligently adding control points where needed. This demand-driven approach enhances imaging fidelity and efficiency for advanced semiconductor manufacturing.

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    Area of Science:

    • Semiconductor Manufacturing
    • Computational Lithography
    • Applied Mathematics

    Background:

    • Advanced technology nodes require curvilinear optical proximity correction (OPC).
    • Conventional OPC methods use uniform or static control point distributions.
    • This leads to inefficient resource use and reduced imaging fidelity due to neglecting regional correction demands.

    Purpose of the Study:

    • To introduce a demand-driven dynamic control point insertion framework for curvilinear OPC.
    • To overcome the inefficiencies of static control point distribution in OPC.
    • To improve both the fidelity and efficiency of curvilinear OPC.

    Main Methods:

    • A sparse initial set of control points is used.
    • The framework iteratively identifies critical regions requiring correction.
    • Leveraging B-spline properties, new control points are dynamically inserted and adjacent points adjusted to refine the correction.

    Main Results:

    • The proposed method establishes rational control point distributions.
    • Achieves lower pattern error compared to uniform sampling.
    • Demonstrates up to 20% reduction in control points needed, indicating superior efficiency and fidelity.

    Conclusions:

    • The dynamic control point insertion framework effectively addresses flaws in initial control point distribution for curvilinear masks.
    • The method offers a more efficient and high-fidelity solution for advanced technology nodes.
    • This approach optimizes resource allocation in optical proximity correction.