The precise control over optical pulse parameters in fiber systems is crucial in many applications. Our research focuses on optimizing optical femtosecond pulses for nonlinear optics, addressing challenges in fiber-based systems with dispersion and nonlinearity. Utilizing spectral phase control and optimization algorithms like particle swarm and simulated annealing, we fine-tune a complex phase mask for desired pulse shapes. Our method involves custom phase-profile optimization via spectral-domain phase modulation to compensate for nonlinear effects in pulse delivery. Using a chirped femtosecond source and a fiber amplifier, our implemented optimization scheme produces near-transform-limited pulses after propagation in polarization-maintaining fiber. This approach accommodates diverse pulse durations, showcasing the effectiveness of off-the-shelf programmable components with optimization algorithms in nonlinear optics and optical signal processing applications.
Our research in neuromorphic computing leverages nonlinear optical dynamics to emulate neural network functionalities. In our experiments, we explore supercontinuum generation and other complex wave dynamics for information processing in the optical domain. Utilizing spectral-domain phase modulation and nonlinear femtosecond pulse broadening in multiple nonlinear fibers, we demonstrate effective data encoding and processing followed by a read-out layer training, akin to Extreme Learning Machines. Our benchmarks on diverse datasets showcase the scalability and inference capabilities of our system, and the distinct performance differences of two nonlinear domains, i.e. self-phase modulation and soliton fission. This work opens new avenues in quantifying physics-based analog computing platforms, suggesting implications for green computing, Big Data communications, and intelligent diagnostics.
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