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Related Experiment Video

Updated: Jul 31, 2025

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
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Process optimization of line patterns in extreme ultraviolet lithography using machine learning and a simulated

Rongbo Zhao, Ziyu Hu, Xiaolin Wang

    Applied Optics
    |May 3, 2023
    PubMed
    Summary

    This study introduces an advanced lithography process optimization system. It enhances resist imaging performance by optimizing resolution, line edge/width roughness, and sensitivity (RLS) indicators for better line patterns.

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    Related Experiment Videos

    Last Updated: Jul 31, 2025

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    Area of Science:

    • Semiconductor manufacturing
    • Materials science
    • Computational modeling

    Background:

    • Critical indicators for resist imaging performance include resolution, line edge/width roughness, and sensitivity (RLS).
    • Shrinking technology nodes demand stricter control over these indicators for high-resolution imaging.
    • Current methods struggle to improve all RLS indicators simultaneously, hindering overall resist performance in extreme ultraviolet lithography.

    Purpose of the Study:

    • To develop a novel system for optimizing lithographic processes for line patterns.
    • To improve the overall imaging performance of resists by addressing multiple RLS indicators concurrently.
    • To accelerate the development of extreme ultraviolet lithography processes.

    Main Methods:

    • Established Resolution, Line edge/width roughness, and Sensitivity (RLS) models using machine learning.
    • Optimized these RLS models with a simulated annealing algorithm.
    • Developed a system to identify optimal process parameter combinations for line patterns.

    Main Results:

    • Achieved high optimization accuracy for resist RLS indicators.
    • Successfully controlled multiple RLS indicators simultaneously for improved line pattern imaging.
    • Demonstrated the system's capability to yield optimal imaging quality.

    Conclusions:

    • The developed lithographic process optimization system effectively controls resist RLS indicators.
    • This approach significantly reduces process optimization time and cost.
    • The system accelerates the advancement of lithography processes for next-generation semiconductor manufacturing.