Total transmission from deep learning designs

Abstract

Total transmission plays an important role in efficiency improvement and wavefront control, and has made great progress in many applications, such as the optical film and signal transmission. Therefore, many traditional physical methods represented by transformation optics have been studied to achieve total transmission. However, these methods have strict limitations on the size of the photonic structure, and the calculation is complex. Here, we exploit deep learning to achieve this goal. In deep learning, the data-driven prediction and design are carried out by artificial neural networks (ANNs), which provide a convenient architecture for large dataset problems. By taking the transmission characteristic of the multi-layer stacks as an example, we demonstrate how optical materials can be designed by using ANNs. The trained network directly establishes the mapping from optical materials to transmission spectra, and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space. Our work paves the way for the optical material design with special properties based on deep learning.

Keywords

• Artificial neural networks (ANNs)
• Deep learning
• Forward spectral prediction
• Inverse material design
• Total transmission