Topological encoding method for data-driven photonics inverse design.

Area of Science:

• Photonics
• Computational electromagnetics
• Machine learning

Background:

• Data-driven methods are increasingly used for photonic device inverse design.
• Current methods face challenges in efficiently representing complex photonic topologies for machine learning.

Purpose of the Study:

• To develop a topological encoding method for representing photonic structures.
• To enable effective dimensionality reduction and dataset generation for data-driven photonic design.
• To assist machine learning in the inverse design of photonic devices.

Main Methods:

• A novel topological encoding method is proposed.
• Photonic structures (binary images) are transformed into a continuous sparse representation.
• The method is applied to design 2D non-paraxial diffractive optical elements.

Main Results:

• The encoding method facilitates dimensionality reduction and dataset generation.
• Successful design of diffractive optical elements with tailored diffraction patterns.
• Demonstrated ability to assist machine-learning-based inverse design for accurate optimization.

Conclusions:

• The proposed topological encoding is effective for data-driven inverse design of photonic structures.
• This method enhances the optimization capabilities of machine learning for photonic device design.
• Enables efficient analysis and global optimization of photonic topologies.