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    This study introduces an experimental genetic algorithm (Exp-GA) to optimize nano-photonic devices, overcoming simulation limitations for complex designs. Exp-GA significantly reduces trial-and-error time and achieves real-world performance enhancements, as demonstrated with thermophotovoltaic emitters.

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    Area of Science:

    • Photonics
    • Materials Science
    • Computational Science

    Background:

    • Nano-photonic device performance is critically dependent on geometry and dimension design.
    • Traditional simulation-based optimization is often ineffective for complex or large-area randomized patterns, such as those in solar cells, LEDs, and thermophotovoltaics (TPVs).
    • Limitations include extended CPU runtime, memory constraints, and challenges with three-dimensional complex structures and anisotropic dielectric responses.

    Purpose of the Study:

    • To introduce a novel optimization scheme by integrating genetic algorithms (GA) with real-world experiments.
    • To address the ineffectiveness of simulation-based optimization for specific nano-photonic design problems.
    • To demonstrate the practical advantages of an experimentally implemented genetic algorithm (Exp-GA) over purely simulation-based approaches.

    Main Methods:

    • Incorporation of genetic algorithm (GA) principles into experimental procedures.
    • Development of an experimentally implemented genetic algorithm (Exp-GA) framework.
    • Application of Exp-GA to optimize TPV emitters as a case study.

    Main Results:

    • Achieved a significant reduction in trial-and-error time for nano-photonic device design.
    • Demonstrated the applicability of Exp-GA to photonic design problems unsuitable for simulation-based optimization.
    • Obtained a 22% enhancement in the mean objective value for TPV emitters, reflecting real-world performance.

    Conclusions:

    • Exp-GA provides a viable and effective alternative to simulation-based optimization for challenging nano-photonic designs.
    • The experimental nature of Exp-GA yields objective values that closely match fabricated device performance, mitigating gaps between theoretical models and reality.
    • This approach offers a pathway to improved performance and efficiency in various nano-photonic applications.