Particle swarm optimization hyperparameters tuning for physical-model fitting of VCSEL measurements

We propose a Particle Swarm Optimization (PSO) algorithm for the extraction of Vertical-Cavity Surface-Emitting Laser (VCSEL) parameters compatible with a rate equation based model that takes into account the thermal effects. PSO is an evolutionary algorithm that drastically reduces the computational cost and time with respect to traditional brute-force approaches, thanks to the "swarm intelligence" of the agents of the optimization (called "particles"). With an optimal choice of the hyperparameters of the algorithm, the method is shown to predict parameters that accurately reproduce the non-linear behavior of the device, as well as its complicated thermal effects.