Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.

Area of Science:

• Optics and Photonics
• Materials Science
• Computational Science

Background:

• Photonic device development (PDD) traditionally relies on computationally intensive methods like Bayesian optimization and physics-driven simulations.
• Challenges in PDD include large optimization landscapes, characterization uncertainties, and fabrication difficulties, hindering scalability and efficiency.

Purpose of the Study:

• To provide a comprehensive overview of machine learning (ML) applications in PDD.
• To highlight how ML-driven strategies can overcome traditional PDD limitations.
• To foster interdisciplinary research for accelerated photonic device innovation.

Main Methods:

• Review of ML techniques including surrogate estimators, generative modeling, reinforcement learning, and active learning.
• Application of ML for accelerating computations, modeling noisy measurements, and optimizing fabrication.
• Integration of ML into the five-step PDD process: design derivation, simulation, optimization, fabrication, and measurement.

Main Results:

• ML offers data-driven solutions to enhance PDD efficiency and overcome computational intractability.
• Generative models improve noisy measurement modeling and data augmentation.
• Reinforcement learning aids in optimizing fabrication processes.

Conclusions:

• Machine-learning-assisted PDD (ML-PDD) significantly accelerates the design and development of complex photonic devices.
• ML integration enables faster simulations, robust characterization under uncertainty, and efficient fabrication.
• This review empowers researchers to leverage ML for advancing photonic technologies across diverse applications.