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Machine Learning based Lithographic Hotspot Detection with Critical-Feature Extraction and Classification

机译：具有关键特征提取和分类的基于机器学习的光刻热点检测

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Duo Ding; Xiang Wu; Joydeep Ghosh; David Z. Pan;
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年度 2011
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1. Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction [J] . Yu Y., Lin G., Jiang I.H., Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on . 2015,第3期

机译：基于机器学习的热点检测，使用拓扑分类和关键特征提取
2. Random forest-based robust classification for lithographic hotspot detection [J] . Rohit Dawar, Samit Barai, Pardeep Kumar, Journal of microanolithography, MEMS, and MOEMS . 2019,第2期

机译：基于随机森林的鲁棒分类用于光刻热点检测
3. Improved Tangent Space-Based Distance Metric for Lithographic Hotspot Classification [J] . Fan Yang, Subarna Sinha, Charles C. Chiang, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems . 2017,第9期

机译：改进的切线空基距离度量用于光刻热点分类
4. Machine learning based lithographic hotspot detection with critical-feature extraction and classification [C] . Duo Ding, Xiang Wu, Ghosh J., IC Design and Technology, 2009. ICICDT '09 . 2009

机译：基于机器学习的具有关键特征提取和分类的光刻热点检测
5. Smartphone-Based Detection of Natural Killer Cells Using Flow-Based Measurement and Machine Learning Classification on Paper Microfluidics [D] . Zenhausern, Ryan. 2021

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7. Inverse Lithography as a DFM Tool: Accelerating Design Rule Development with Model-Based Assist Feature Placement, Fast Optical Proximity Correction and Lithographic Hotspot Detection [O] . Steve Prinsa, James Blatchforda, Simon Changa, 2014

机译：反向光刻作为DFm工具：利用基于模型的辅助特征放置，快速光学邻近校正和光刻热点检测加速设计规则开发

Machine Learning based Lithographic Hotspot Detection with Critical-Feature Extraction and Classification

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