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Register a new userChip-based Brillouin processing for carrier recovery in coherent optical communications
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Modern fiber-optic coherent communications employ advanced spectrally-efficient modulation formats that require sophisticated narrow linewidth local oscillators (LOs) and complex digital signal processing (DSP). Here, we establish a novel approach to carrier recovery harnessing large-gain stimulated Brillouin scattering (SBS) on a photonic chip for up to 116.82 Gbit/sec self-coherent optical signals, eliminating the need for a separate LO. In contrast to SBS processing on-fiber, our solution provides phase and polarization stability while the narrow SBS linewidth allows for a record-breaking small guardband of ~265 MHz, resulting in higher spectral-efficiency than benchmark self-coherent schemes. This approach reveals comparable performance to state-of-the-art coherent optical receivers without requiring advanced DSP. Our demonstration develops a low-noise and frequency-preserving filter that synchronously regenerates a low-power narrowband optical tone that could relax the requirements on very-high-order modulation signaling and be useful in long-baseline interferometry for precision optical timing or reconstructing a reference tone for quantum-state measurements.
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Wavelength-scale SBS waveguides are enabling novel on-chip functionalities. The micro- and nano-scale SBS structures and the complexity of the SBS waveguides require a characterization technique to monitor the local geometry-dependent SBS responses along the waveguide. In this work, we experimentally demonstrate detection of longitudinal features down to 200$mu$m on a silicon-chalcogenide waveguide using the Brillouin optical correlation domain analysis (BOCDA) technique. We provide simulation and analysis on how multiple acoustic and optical modes and geometrical variations influence the Brillouin spectrum.
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We grow accustomed to the notion that optical susceptibilities can be treated as a local property of a medium. In the context of nonlinear optics, both Kerr and Raman processes are considered local, meaning that optical fields at one location do not produce a nonlinear response at distinct locations in space. This is because the electronic and phononic disturbances produced within the material are confined to a region that is smaller than an optical wavelength. By comparison, Brillouin interactions can result in a highly nonlocal nonlinear response, as the elastic waves generated through the Brillouin process can occupy a region in space much larger than an optical wavelength. The nonlocality of these interactions can be exploited to engineer new types of processes, where highly delocalized phonon modes serve as an engineerable channel that mediates scattering processes between light waves propagating in distinct optical waveguides. These types of nonlocal optomechanical responses have been recently demonstrated as the basis for information transduction, however the nontrivial dynamics of such systems has yet to be explored. In this work, we show that the third-order nonlinear process resulting from spatially extended Brillouin-active phonon modes involves mixing products from spatially separated, optically decoupled waveguides, yielding a nonlocal joint-susceptibility. We further explore the coupling of multiple acoustic modes and show that multi-mode acoustic interference enables a tailorable nonlocal-nonlinear susceptibility, exhibiting a multi-pole frequency response.
Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep neural networks to classify numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.
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