Intelligent Laser Micro/Nano Processing: Research and Advances

作者：Yuxin Liu, Wei Gong, F. Bu, Xinjing Zhao, Hang Li, Weiwei Xu, Aiwu Li, Guohong Liu, Tao An, Bing‐Rong Gao · 发表于：Nanomaterials · 年份：2025 · DOI：10.3390/nano15191462 · 被引用次数：6 · 研究领域：Laser Material Processing Techniques、Additive Manufacturing Materials and Processes、Laser-induced spectroscopy and plasma

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser-matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field.