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Engineered multiwavelength conversion using nonperiodic optical superlattice optimized by genetic algorithm.

Jui-Yu Lai1, Yi-Jhen Liu, Hung-Yu Wu

  • 1Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan.

Optics Express
|April 15, 2010
PubMed
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We developed a new method using a genetic algorithm to optimize nonperiodic optical superlattices (NOS) for multiwavelength conversion. This approach enhances conversion efficiency and spectral fidelity compared to existing methods.

Area of Science:

  • Photonics and Optical Engineering
  • Materials Science
  • Computational Physics

Background:

  • Mutiwavelength conversion is crucial for optical communication systems.
  • Existing methods like aperiodic optical superlattices have limitations in efficiency and spectral fidelity.
  • Optimization of optical superlattices is key to improving device performance.

Purpose of the Study:

  • To propose and demonstrate a novel scheme for flexible multiwavelength conversion.
  • To optimize nonperiodic optical superlattices (NOS) using a genetic algorithm with dual target functions.
  • To compare the proposed scheme with the aperiodic optical superlattice approach.

Main Methods:

  • Utilizing a genetic algorithm with two target functions for NOS optimization.

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  • Implementing a new scheme for flexible multiwavelength conversion.
  • Conducting numerical analysis and experimental fabrication of NOS devices.

    • Main Results:

    • Achieved approximately 15% higher overall conversion efficiency compared to aperiodic optical superlattices.
    • Demonstrated improved spectral fidelity in multiwavelength conversion.
    • Numerical analysis showed insensitivity to fabrication errors and predictable distortion under pump depletion.

    Conclusions:

    • The proposed genetic algorithm-optimized NOS scheme offers superior performance for multiwavelength conversion.
    • The method allows for further performance enhancements with larger genetic pools.
    • Experimental results validate the numerical predictions and the effectiveness of the optimization approach.