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Inverse design of photonic nanostructures using dimensionality reduction: reducing the computational complexity
Optics Letters
|June 1, 2021
Summary
We developed a deep learning method using neural networks (NNs) for photonic nanostructure inverse design. Dimensionality reduction significantly cuts computational costs for designing thin-film structures.
Area of Science:
- Photonics
- Materials Science
- Computational Science
Background:
- Inverse design of photonic nanostructures is computationally intensive.
- Neural networks (NNs) offer a promising approach for complex design problems.
Purpose of the Study:
- To present a deep-learning-based method for efficient inverse design of photonic nanostructures.
- To reduce the computational complexity of inverse design algorithms.
Main Methods:
- Utilized neural networks (NNs) for inverse design.
- Implemented dimensionality reduction in both design and response spaces.
- Applied the method to design multi-layer thin-film dielectric structures.
Main Results:
- Achieved considerable reduction in computational complexity.
- Demonstrated the effectiveness of the method for thin-film structure design.
- Compared results with conventional NN approaches.
Conclusions:
- Deep learning with dimensionality reduction offers an efficient approach for photonic nanostructure inverse design.
- The proposed method significantly reduces computational burden compared to conventional techniques.
