Particle swarm optimization-assisted approach for the extraction of VCSEL model parameters.

Area of Science:

• Optics and Photonics
• Computational Physics
• Laser Engineering

Background:

• Accurate modeling of vertical-cavity surface-emitting lasers (VCSELs) is crucial for device design and performance prediction.
• Extracting physical model parameters from experimental data (light-current and modulation responses) is a challenging optimization problem.
• Existing nonlinear optimization methods may face limitations in parameter extraction for complex laser dynamics.

Purpose of the Study:

• To introduce a direct particle swarm optimization (PSO) method for efficient parameter extraction of VCSEL physical models.
• To validate the PSO method's ability to accurately reproduce VCSEL behavior using light-current (L-I) and small-signal modulation (S21) data.
• To benchmark the performance of the proposed PSO method against established nonlinear optimization techniques.

Main Methods:

• A direct particle swarm optimization (PSO) algorithm was employed for parameter extraction.
• The method utilized light-current (L-I) characteristics and small-signal modulation (S21) responses as input data.
• Hyperparameter tuning of the PSO algorithm was performed to optimize its predictive capabilities.

Main Results:

• The PSO method successfully extracted physical model parameters that accurately reproduced VCSEL device behavior.
• Optimal hyperparameters significantly enhanced the predictive accuracy of the PSO approach.
• The performance of the PSO method was quantitatively compared against Interior Point and Levenberg-Marquardt optimizers.

Conclusions:

• Direct particle swarm optimization offers a robust and accurate method for VCSEL parameter extraction.
• The PSO approach demonstrates superior or comparable performance to traditional nonlinear optimizers for VCSEL modeling.
• This method provides a valuable tool for advancing the understanding and design of VCSEL devices.