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• W4387496766 abstract "Abstract Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization." @default.
• W4387496766 created "2023-10-11" @default.
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• W4387496766 date "2023-10-10" @default.
• W4387496766 modified "2023-10-14" @default.
• W4387496766 title "Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning" @default.
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• W4387496766 doi "https://doi.org/10.1038/s41378-023-00587-z" @default.
• W4387496766 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37829156" @default.
• W4387496766 hasPublicationYear "2023" @default.
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