Optical neuromorphic computing marks a breakthrough over traditional digital computing by offering energy-efficient, fast, and parallel processing solutions while challenges remain in incorporating nonlinearity efficiently. Leveraging nonlinear wave dynamics in optical fibers as a computational resource may provide a solution. Our research demonstrates how femtosecond pulse propagation in optical fibers can emulate neural network inference, utilizing the high phase sensitivity of broadband light for creating nonlinear input-output mappings akin to Extreme Learning Machines (ELMs). Experimental results show high classification accuracies and low RMS errors in function regression, all at pico-joule pulse energy. This indicates our method's potential to lower energy consumption for inference tasks, complementing existing spatial-mode systems. We also investigated femtosecond pulses' nonlinear broadening effects – self-phase modulation and coherent soliton fission – demonstrating their distinct impacts on classification tasks and showcasing broadband frequency generation as a powerful, energy-efficient tool for next-generation computing.
Our latest work in softphotonics features the seamless integration of liquid-core fibers into standard fiber systems, achieving unprecedented advancements in nonlinear optics and signal processing. Utilizing liquids like CS2 and halocarbonates, we have developed a reliable splicing technique with standard fibers, enabling low-loss all-fiber interconnectivity. This breakthrough provides insights into intra-fiber thermodynamics and enables robust, long-term applications in optical signal processing in the near-infrared. Our method opens new avenues for exploring negative pressures and facilitating multi-laser excitation for cross-phase modulation, showcasing the potential of liquid-filled fibers in future optical technologies.
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