Deep learning in light-matter interactions.

Area of Science:

• Photonics
• Artificial Intelligence
• Nanotechnology

Background:

• Deep learning offers novel methods for manipulating light across various scales.
• It enables the creation of predictive models for light-matter interactions using extensive datasets.
• Applications include enhancing nanophotonic device design and optimizing experimental data acquisition and analysis.

Purpose of the Study:

• To provide an overview of current deep learning applications in photonics.
• To discuss the emerging opportunities presented by deep learning in this field.
• To highlight the challenges and limitations associated with deep learning in photonics research.

Main Methods:

• Utilizing large experimental and simulated datasets to train deep learning models.
• Developing models for light-matter interactions.
• Analyzing existing literature on deep learning in photonics.

Main Results:

• Deep learning has successfully improved nanophotonic device design.
• It enhances the acquisition and analysis of experimental photonic data.
• Challenges include the interpretability and reliability of deep learning models, especially with incomplete or adversarial data.

Conclusions:

• Deep learning presents significant opportunities for advancing photonics.
• Addressing the "black box" nature of deep learning is crucial for its reliable application.
• Further research is needed to overcome challenges and fully harness deep learning's potential in photonics.