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    A new algorithm, RMS-log-sum-OPC, enhances lithographic fidelity and computational efficiency in mask optimization for VLSI manufacturing. This method balances accuracy and speed, crucial for advanced technology nodes.

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    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Materials Science

    Background:

    • Mask optimization is critical for advanced VLSI manufacturing.
    • Optical proximity correction (OPC) is a key resolution enhancement technique in lithography.
    • Balancing lithographic accuracy and computational efficiency is a major challenge in OPC.

    Purpose of the Study:

    • To introduce a novel algorithm, RMS-log-sum-OPC, for fast and high-fidelity optical proximity correction.
    • To address the trade-off between computational efficiency and lithographic accuracy in OPC.

    Main Methods:

    • Proposed a logarithmic and regularized iterative threshold algorithm incorporating the RMSprop method (RMS-log-sum-OPC).
    • Implemented OPC by solving a series of logarithmic and regularized reconstruction problems with iterative weight updates.
    • Utilized RMSprop's dynamic learning rate for convergence speed and stability, preserving logarithmic sparsity.

    Main Results:

    • RMS-log-sum-OPC improved lithographic fidelity by 44.7% and 19.6% on two target layouts compared to BCS-OPC.
    • Reduced running time per iteration by 6.67% and 2.48% respectively, demonstrating enhanced computational efficiency.
    • The algorithm is suitable for high-dimensional, non-convex, sparse problems inherent in OPC.

    Conclusions:

    • RMS-log-sum-OPC achieves both high lithographic fidelity and computational efficiency in mask optimization.
    • The proposed method offers a significant advancement over existing techniques like BCS-OPC.
    • This algorithm is well-suited for complex, large-scale OPC applications in advanced VLSI manufacturing.