Parameter extraction and inverse design of semiconductor lasers based on the deep learning and particle swarm...

Area of Science:

• Optoelectronics
• Artificial Intelligence
• Materials Science

Background:

• Semiconductor laser design traditionally faces challenges in accuracy and computational efficiency.
• Existing artificial neural network (NN) methods can suffer from single-solution limitations.
• Device failure analysis requires precise parameter extraction for accurate diagnostics.

Purpose of the Study:

• To develop a highly accurate and computationally fast method for the inverse design of semiconductor lasers.
• To address the single-solution problem often encountered with tandem NN approaches.
• To facilitate the extraction of critical parameters for device failure analysis.

Main Methods:

• Integration of a deep-learning artificial neural network (NN) with the particle swarm optimization (PSO) algorithm.
• Utilizing the combined NN-PSO method for inverse design of semiconductor laser parameters.
• Validation against benchmarks from the traveling-wave model for light-current curves and small signal responses.

Main Results:

• The proposed NN-PSO method demonstrated high accuracy and computational speed in semiconductor laser inverse design.
• The approach successfully avoided the single-solution problem inherent in some tandem NN techniques.
• Simulations showed robustness and efficiency in predicting laser behavior, validated by traveling-wave model benchmarks.

Conclusions:

• The NN-PSO hybrid method offers a powerful tool for accurate and efficient semiconductor laser inverse design.
• This technique is valuable for identifying problematic parameters in device failure analysis.
• The validated simulation capabilities support further advancements in laser device engineering.