Modeling extended L-band fiber amplifiers using neural networks trained on experimental data.

Area of Science:

• Optical Engineering
• Computational Physics
• Machine Learning Applications

Background:

• Accurate numerical models are crucial for high-performance amplifier design.
• Parameter-based models for L-band amplifiers face accuracy limitations due to challenges in Giles-parameter estimation.
• Computationally efficient models are highly valuable given the large amplifier optimization space.

Purpose of the Study:

• To develop a high-accuracy, computationally efficient neural network model for L-band amplifiers.
• To overcome the limitations of parameter-based models in amplifier design.
• To demonstrate the application of neural network models in optimizing amplifier performance.

Main Methods:

• Utilized a rich, experimentally captured dataset to train neural network models.
• Developed independent neural network models for amplifier gain and noise figure.
• Created separate models for core and cladding pumping methods.
• Employed neural networks to avoid complex parameterization issues.

Main Results:

• Neural network models demonstrated superior accuracy compared to parameter-based models, reducing error variance by 50%.
• Achieved extremely fast simulation times, approximately 400 times faster than traditional methods.
• Successfully designed an amplifier optimizing optical signal-to-noise ratio using exhaustive search with the developed models.

Conclusions:

• Neural network models offer a significant advancement in amplifier design, providing higher accuracy and speed.
• The developed approach facilitates complex optimization tasks, such as maximizing optical signal-to-noise ratio.
• This methodology greatly accelerates the amplifier design process, enabling more efficient research and development.