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Edge-enhanced optical parametric generation in periodically poled LiNbO3.

Nicklas Bjärnhall Prytz, Daniel Qvarngård, Antti Härkönen

    Optics Express
    |July 19, 2020
    PubMed
    Summary

    Researchers observed significantly higher optical parametric gains at the edge of periodically poled lithium niobate (PPLN) crystals. This edge effect, observed in MgO-doped PPLN, led to a 3.6-fold increase in output power compared to internal PPLN generation.

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    Area of Science:

    • Nonlinear Optics
    • Materials Science
    • Quantum Optics

    Background:

    • Periodically poled lithium niobate (PPLN) is a key material for nonlinear optical frequency conversion.
    • Understanding spatial effects in PPLN is crucial for optimizing parametric processes.
    • Previous studies have focused on bulk PPLN properties, with less attention to edge effects.

    Purpose of the Study:

    • To investigate and quantify enhanced optical parametric gains at the edges of PPLN regions.
    • To compare experimental results with numerical simulations of nonlinear wave dynamics.
    • To explore the potential for improved parametric generation efficiency by utilizing PPLN edges.

    Main Methods:

    • Experiments were conducted using MgO-doped PPLN samples.
    • Optical parametric generation was driven by a 532 nm pump laser.

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  • Numerical simulations modeled nonlinear wave propagation assuming a refractive index increase (Δn = 5.3 × 10-5) within the PPLN region.

    • Main Results:

    • Enhanced optical parametric gains were observed specifically at the PPLN region's edge.
    • Parametric signal outputs were measured around 800 nm and 1550 nm.
    • Excitation near the PPLN edge with 8.1 mW pump power resulted in a 3.6-fold increase in output power compared to excitation within the PPLN area.

    Conclusions:

    • The edge of PPLN regions exhibits significantly enhanced optical parametric gain.
    • Experimental findings align well with numerical simulations, validating the model.
    • Exploiting PPLN edges offers a promising route for boosting parametric generation efficiency.