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Many uses of lasers place the highest importance on access to specific wavelength bands. For example, mobilizing optical-atomic clocks for a leap in sensing requires compact lasers at frequencies spread across the visible and near-infrared. Integrated photonics enables high-performance, scalable laser platforms. However, customizing laser-gain media to support wholly new bands is challenging and often prohibitively mismatched in scalability to early quantum-based sensing and information systems. Here, we demonstrate a tantalum pentoxide microresonator optical-parametric oscillator (OPO) that converts a pump laser to an output wave within a frequency span exceeding an octave. We control phase matching for oscillation by nanopatterning the microresonator to open a photonic-crystal bandgap on the mode of the pump laser. The photonic crystal splits only the pump mode and preserves the broader mode structure of the resonator, thus affording a single parameter to control output waves across the octave span using a nearly fixed frequency pump laser. We also demonstrate tuning the oscillator in free-spectral-range steps, more finely with temperature, and minimal additive frequency noise of the laser-conversion process. Our work shows that nanophotonic structures offer control of laser conversion in microresonators, bridging phase-matching of nonlinear optics and application requirements for laser designs.
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Multi-modality deep learning for pulse prediction in homogeneous nonlinear systems via parametric conversion
In this Letter, we introduce FusionNet, a multi-modality deep learning framework designed to predict and analyze output pulses in high-power rare-earth-doped laser systems driving parametric conversion in homogeneous guided nonlinear media. FusionNet integrates temporal, spectral, and physical experimental conditions to model ultrafast nonlinear phenomena, including parametric nonlinear frequency conversion, self-phase modulation, and cross-phase modulation in homogeneous guided systems such as gas-filled hollow-core fibers. These systems bridge physical models with experimental data, advancing our understanding of light-guiding principles and nonlinear interactions while expediting the design and optimization of on-demand high-power, high-brightness systems. Our results demonstrate a 73% reduction in prediction error and an 83% improvement in computational efficiency compared to conventional neural networks. This work establishes a new paradigm for accelerating parametric simulations and optimizing experimental designs in high-power laser systems, with further implications for high-precision spectroscopy, quantum information science, and distributed entangled interconnects.
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- Award ID(s):
- 2436343
- PAR ID:
- 10588645
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Photonics
- Volume:
- 10
- Issue:
- 5
- ISSN:
- 2378-0967
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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