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

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 (R 2 ) 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.