4.7 Article

Ultralow-threshold thin-film lithium niobate optical parametric oscillator

Journal

OPTICA
Volume 8, Issue 4, Pages 539-544

Publisher

OPTICAL SOC AMER
DOI: 10.1364/OPTICA.418984

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Funding

  1. U.S. Department of Energy [DE-SC0019406]
  2. National Science Foundation [EFMA-1640959]
  3. David and Lucile Packard Foundation [2009-34719]
  4. U.S. Department of Energy (DOE) [DE-SC0019406] Funding Source: U.S. Department of Energy (DOE)

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This research presents a chip-scale lithium niobate optical parametric oscillator with a high-quality microring resonator, achieving a significantly lower threshold power and higher power conversion efficiency compared to previous integrated photonics platforms. The OPO allows wavelength tuning by adjusting pump frequency and chip temperature, offering new opportunities for chip-based tunable light sources and photonic neural networks.
Materials with strong second-order (chi((2))) optical nonlinearity, especially lithium niobate, play a critical role in building optical parametric oscillators (OPOs). However, chip-scale integration of low-loss chi((2)) materials remains challenging and limits the threshold power of on-chip chi((2)) OPO. Herewe report an on-chip lithium niobate optical parametric oscillator at the telecom wavelengths using a quasi-phase-matched, high-quality microring resonator, whose threshold power (similar to 30 mu W) is 400 times lower than that in previous chi((2)) integrated photonics platforms. An on-chip power conversion efficiency of 11% is obtained from pump to signal and idler fields at a pump power of 93 mu W. The OPO wavelength tuning is achieved by varying the pump frequency and chip temperature. With the lowest power threshold among all on-chipOPOs demonstrated so far, aswell as advantages including high conversion efficiency, flexibility in quasi-phase-matching, and device scalability, the thin-film lithium niobate OPO opens new opportunities for chip-based tunable classical and quantum light sources and provides a potential platform for realizing photonic neural networks. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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