Neural Network Enabled Real-Time Plasma Imaging for Inverse Designed Fabrication of Micro/Nano Structures with Ultrafast Laser.

Abstract

Real-time monitoring is essential for quality control in highprecision micro/nano structure fabrication with ultrafast laser. The laser-induced plasma plume blocks the sample surface, preventing direct imaging of micro/nano structures during processing. This work proposes a neural networkenabled real-time plasma imaging strategy and inverse design micro/nano fabrication method. The neural networks contain a conditional generative adversarial network (cGAN) for real-time imaging and a multilayer perceptron (MLP) for inverse-designed fabrication. Based on the fabrication of coffee-ring feature structures, the cGAN generates high-fidelity feature structure images from plasma intensity profiles, with a real-time imaging latency of 1691 milliseconds. To validate the real-time imaging strategy, dual-pulse processing and sequential single-pulse processing experiments are conducted across different materials. The MLP model establishes nonlinear relationships between laser parameters and feature structure size for inverse-designed fabrication. Both forward prediction and inverse design results achieved a coefficient of determination (R²) of 0.97, and the actual fabrication results align with the target values. This work provides a neural networkenabled strategy for real-time process monitoring, advancing the development of intelligent laser fabrication.