Unveiling the relationship between Fabry-Perot laser structures and optical field distribution via symbolic regression

Abstract

In recent years, machine learning (ML) techniques have been shown to be effective in accelerating the development process of optoelectronic devices. However, as “black box” models, they have limited theoretical interpretability. In this work, we leverage symbolic regression (SR) technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot (FP) laser and its optical field distribution, which greatly improves model transparency compared to ML. We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models, which suggests that the expressions have better fitting and generalization capabilities.

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