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    Area of Science:

    • Semiconductor Manufacturing
    • Computational Lithography
    • Applied Mathematics

    Background:

    • Optical proximity correction (OPC) is crucial for resolution enhancement in optical lithography.
    • Pixelated OPC methods face computational challenges due to complex mask designs in modern integrated circuits.

    Purpose of the Study:

    • To enhance the computational efficiency of gradient-based pixelated OPC methods.
    • To apply nonlinear compressive sensing (CS) theory to address computational bottlenecks in OPC.

    Main Methods:

    • Downsampling the layout pattern to reduce problem dimensionality.
    • Establishing a nonlinear cost function for lithography imaging performance on downsampled layouts.
    • Formulating the OPC problem as an inverse nonlinear CS reconstruction problem under mask sparsity.

    Main Results:

    • Improved computational efficiency compared to traditional gradient-based OPC methods.
    • Enhanced process windows in lithography systems.
    • Improved mask manufacturability due to exploiting mask pattern sparsity.

    Conclusions:

    • Nonlinear CS theory offers a breakthrough for computationally efficient pixelated OPC.
    • The proposed method achieves superior lithography performance and mask manufacturability.
    • This approach represents a significant advancement in integrated circuit mask design and fabrication.