Diffractive neural networks with polynomial phase masks for laser beam shaping with quasi-continuous diffractive...

Area of Science:

• Optics and Photonics
• Machine Learning Applications
• Optical Engineering

Background:

• Diffractive optical elements (DOEs) are crucial for laser beam shaping.
• Designing accurate DOEs, especially continuous ones, can be challenging.
• Existing methods may struggle with accuracy or adaptability to manufacturing.

Purpose of the Study:

• To introduce a novel, accurate, and adaptable method for designing continuous diffractive optical elements (DOEs).
• To leverage diffractive neural networks (DNNs) for laser beam shaping applications.
• To validate the design approach with continuous reflective DOEs under manufacturing constraints.

Main Methods:

• Utilized diffractive neural networks (DNNs) with trainable polynomial phase masks.
• Developed a design methodology for both single and cascaded phase masks.
• Adapted the DNN approach to account for specific manufacturing conditions of continuous reflective DOEs.

Main Results:

• Achieved consistently high beam shaping accuracy irrespective of the initial design guess.
• Demonstrated successful adaptation of the DNN design method for continuous reflective DOEs.
• Experimentally verified the performance of the designed DOEs in a two-element optical setup.

Conclusions:

• The proposed DNN-based approach offers a robust and accurate method for designing continuous DOEs.
• This technique is adaptable to real-world manufacturing constraints, enabling practical applications.
• The study validates the effectiveness of DNNs for advanced laser beam shaping tasks.