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Machine Learning-Driven Optimization of Burst Femtosecond Laser Processing for High-Performance Anti-Reflective

Yulong Ding¹, Cong Wang¹, Xianshi Jia¹

¹State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

ACS Applied Materials & Interfaces

|November 18, 2025

Summary

This summary is machine-generated.

Machine learning accelerates femtosecond laser fabrication of anti-reflective microstructures. This innovation enhances efficiency and performance for applications in materials science and optical engineering.

Keywords:

anti-reflective microstructures femtosecond laser machine learning process optimization real-time prediction

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Area of Science:

Materials Science
Optical Engineering
Biomedicine

Background:

Femtosecond laser processing enables micro/nanostructure fabrication for diverse applications.
Current methods rely on inefficient trial-and-error, hindering high-performance manufacturing.

Purpose of the Study:

To develop an efficient machine learning (ML) strategy for real-time prediction and optimization of femtosecond laser fabrication.
To achieve high-performance anti-reflective microstructures with improved efficiency.

Main Methods:

Trained a multilayer perceptron model using finite-difference time-domain simulation data.
Established a nonlinear mapping between microstructural parameters and transmittance.
Integrated the ML model into the fabrication system for rapid prediction (0.004 s).

Main Results:

Achieved efficient manufacturing of large-area (12 × 12 mm²) anti-reflective microhole arrays on MgF₂ windows.
Fabricated structures at a rate of 10,000 holes per second with 2 μm periodicity.
Obtained an average transmittance of 99.03% (3-5 μm range) with broad angular stability (0-50°).

Conclusions:

ML-assisted femtosecond laser processing significantly enhances fabrication efficiency and microstructure performance.
The developed anti-reflective MgF₂ windows demonstrate excellent infrared imaging capabilities.
This approach facilitates the deployment of advanced optical components in demanding environments.