Intelligent Laser Micro/Nano Processing: Research and Advances

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.

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Publication Details

Journal
Nanomaterials
Published
2025-09-23
DOI
https://doi.org/10.3390/nano15191462
Citations
1
Primary Topic
Laser Material Processing Techniques
Type
article
Field-Weighted Citation Impact
1.33

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article

Intelligent Laser Micro/Nano Processing: Research and Advances

Yuxin Liu, Hang Li, Wei Gong, Weiwei Xu et al.
1 citations
Nanomaterials
Laser Material Processing Techniques
1.33
article

Intelligent Laser Micro/Nano Processing: Research and Advances

Yuxin Liu, Hang Li, Wei Gong, Weiwei Xu, Xinjing Zhao, Guohong Liu, Aiwu Li, F. Bu, Bing‐Rong Gao, Tao An
article en
1 citations

Abstract

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.

NanomaterialsVol. 15(19)
Jilin University (CN), State Key Laboratory on Integrated Optoelectronics (CN), Jilin Engineering Normal University (CN), Changchun University (CN)
Natural Science Foundation of Jilin Province, National Natural Science Foundation of China, People's Government of Jilin Province, National Key Research and Development Program of China
Openalex Percentile: Top 18%
Laser Material Processing Techniques
1.33
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