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Graphic-processable deep neural network for the efficient prediction of 2D diffractive chiral metamaterials
Applied Optics
|July 15, 2021
Summary
We developed a graphic-processable deep neural network (DNN) to predict optical chirality in 2D chiral metamaterials. This AI tool accelerates calculations by 10,000x compared to traditional methods, enabling faster discovery of novel nanostructures.
Area of Science:
- Nanophotonics and Metamaterials
- Computational Electromagnetics
- Artificial Intelligence in Materials Science
Background:
- Chiral metamaterials offer unique optical properties but their design and analysis are computationally intensive.
- Predicting optical chirality, especially in higher-order diffractions, requires sophisticated simulation techniques.
- Existing methods often struggle with arbitrary geometries and diverse material compositions.
Purpose of the Study:
- To develop a novel graphic-processable deep neural network (DNN) for automated prediction and elucidation of optical chirality in 2D diffractive chiral metamaterials.
- To explore the influence of material composition (Au, Ag, Al, Cu), thickness, and arbitrary geometries on chiroptical responses.
- To accelerate the computational speed for metamaterial analysis significantly.
Main Methods:
- A graphic-processable deep neural network (DNN) was designed to accept 2D images encoding material, thickness, and geometric parameters.
- Rigorous Coupled Wave Analysis (RCWA) was employed to calculate circular dichroism (CD) and train the DNN.
- The DNN was trained using encoded images of four classes of 2D chiral metamaterials.
Main Results:
- The DNN accurately predicts optical chirality and chiroptical responses for various metamaterial designs.
- A 10,000-fold acceleration in computing speed compared to RCWA was achieved.
- The study identified that the smallest intensity in third-order diffraction beams of E-like metamaterials corresponds to the largest CD response.
Conclusions:
- The graphic-processable DNN offers a powerful and efficient tool for designing and analyzing complex 2D chiral metamaterials.
- This approach enables the exploration of a wider design space, including arbitrary shapes and material combinations.
- The findings pave the way for future research in advanced nanostructures and nonlinear optical devices.
