Hexagonal diffraction gratings generated by convolutional neural network-based deep learning for suppressing...

Area of Science:

• Optics and Photonics
• Materials Science
• Artificial Intelligence

Background:

• Traditional diffraction gratings struggle with non-monochromatic light, leading to superimposed higher-order diffractions that complicate spectral analysis.
• Existing methods for spectral decoding are hindered by the presence of unwanted diffraction orders, impacting precision and efficiency.

Purpose of the Study:

• To design and demonstrate single-order diffraction gratings, termed hexagonal diffraction gratings (HDGs), capable of suppressing higher-order diffractions.
• To utilize a deep learning algorithm, specifically a convolutional neural network, for the precise design and parameter retrieval of HDGs.

Main Methods:

• Development of a convolutional neural network (CNN) model for designing HDGs with sinusoidal transmittance.
• Training the CNN to accurately determine the structural parameters essential for HDG functionality.
• Conducting simulations and experimental validations to assess the diffraction suppression capabilities of the designed HDGs.

Main Results:

• The designed HDGs effectively suppress higher-order diffractions, particularly those above the third order.
• The intensity of third-order diffraction was significantly reduced from 20% to below background levels compared to the first-order diffraction.
• The CNN model demonstrated high accuracy in retrieving the structural parameters required for HDG fabrication.

Conclusions:

• Hexagonal diffraction gratings offer a novel solution for suppressing higher-order diffractions in spectral analysis.
• The integration of deep learning accelerates the design and optimization of advanced optical components like HDGs.
• HDGs show significant promise for enhancing spectral decoding in demanding fields such as synchrotron radiation, astrophysics, and soft x-ray lasers.