Particle swarm optimization for the design and characterization of silica-based photonic crystal fiber amplifiers

In recent years, the development of photonic crystal fibers has allowed novel opportunities for enhancing optical amplifier characteristics. In this field, accurate numerical modeling is a significant need to predict the device behavior. Conventional approaches perform this task by using methods which could yield solutions characterized by divergent or unstably convergent algorithms. Global optimization methods can be considered as efficient tools to face this problem.In this paper, the application of particle swarm optimization to perform the design and characterization of photonic crystal fiber amplifiers is proposed. The employment of this technique shows different attractive features. In particular, solutions are found quickly and the implementation of the algorithm does not require complicated evolutionary operators. Numerical results show the effectiveness of the approach for both the design and characterization of a fiber amplifier. In fact, if considered as a design tool, the obtained numerical results are in good accordance with respect to the ones yielded by a conventional approach. If considered as a characterization tool, the algorithm performs a forecasting, allowing to determine parameters, such as homogeneous upconversion coefficients, whose computation could present difficulties when it is obtained via direct or indirect measurements.

Research Highlights
► Employment of a method based on swarm intelligence as an optimization tool.
► Application of the method to the design of erbium doped PCF amplifiers.
► Computation of the optimal parameters maximizing the gain of a fiber amplifier.
► Solution of the fiber amplifier characterization problem.