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Nonvolatile multilevel adjustable optical switch based on plasmonic slot waveguide and GST segmented structure.

Yiqun Zhang, Qiong Duan, Xu Yan

    Optics Express
    |June 11, 2024
    PubMed
    Summary

    This study introduces a novel non-volatile optical switch using phase-change materials for efficient optical computing. The new design achieves high transmittance differences, enabling accurate weight updates for optical neural networks.

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    Area of Science:

    • Photonics and Optical Engineering
    • Materials Science
    • Computer Science

    Background:

    • Optical computing offers advantages for complex computations but is hindered by traditional optical switch limitations.
    • Existing optical switches suffer from high power consumption, slow switching speeds, and lack of compactness.
    • These limitations impede the development of large-scale photonic integrated circuits and optical neural networks.

    Purpose of the Study:

    • To propose and demonstrate an innovative non-volatile multi-level adjustable optical switch.
    • To overcome the performance limitations of traditional optical switches.
    • To enable efficient optical neural networks and memory computing.

    Main Methods:

    • Designed a novel optical switch combining a plasmonic slot waveguide with segmented phase-change materials (Ge2Sb2Te5 - GST).
    • Utilized the phase state modulation of GST to control waveguide light transmission.
    • Investigated optical transmittance at a 1550 nm wavelength.

    Main Results:

    • Achieved a low insertion loss of 0.5 dB.
    • Demonstrated an 85% difference in optical transmittance between amorphous (aGST) and crystalline (cGST) states.
    • Successfully implemented a handwritten digit recognition task with 95% accuracy.

    Conclusions:

    • The proposed optical switch design offers a significant improvement over traditional methods.
    • The high transmittance difference enables wide-ranging weight variations and precise updates for optical neural networks.
    • This work lays the foundation for future efficient memory computing and neural morphic networks.