Latent space-based modeling for spectral prediction in generative photonics design.

Area of Science:

• Optics and Photonics
• Materials Science
• Computational Electromagnetics

Background:

• Electromagnetic (EM) metasurfaces offer advanced light manipulation capabilities for novel applications.
• Traditional optical response calculations using full-wave EM solvers are computationally expensive.
• Machine learning (ML) surrogate models are explored to accelerate metasurface design, facing challenges in data efficiency and design diversity.

Purpose of the Study:

• To investigate a latent representation-based encoding approach for metasurface unit cell structures.
• To develop an ML model for efficient and accurate optical response prediction.
• To assess the data efficiency and shape diversity preservation of the proposed ML model.

Main Methods:

• Utilized a latent space encoding technique to represent the geometric patterns of metasurface unit cells.
• Developed and trained a machine learning model using this latent representation for optical response prediction.
• Evaluated the model's performance in terms of data efficiency and its ability to capture diverse nanostructure designs.

Main Results:

• The latent space-based ML model demonstrated high data efficiency in predicting optical responses.
• The approach successfully preserved the diversity of possible nanostructure shapes.
• This method offers a significant acceleration compared to traditional full-wave EM solvers.

Conclusions:

• Latent representation-based encoding is an effective strategy for developing data-efficient ML surrogate models for metasurface design.
• This approach accelerates the computational design of electromagnetic metasurfaces.
• The method facilitates exploration of diverse nanostructure designs, crucial for novel optical applications.