[go: up one dir, main page]

Shin et al., 2016 - Google Patents

Accurate lithography hotspot detection using deep convolutional neural networks

Shin et al., 2016

Document ID
4098971489718989817
Author
Shin M
Lee J
Publication year
Publication venue
Journal of Micro/Nanolithography, MEMS, and MOEMS

External Links

Snippet

As the physical design of semiconductors continues to shrink, the lithography process is becoming more sensitive to layout design. Identifying lithography hotspots (HSs) in the layout design stage appears to be more and more crucial for fast semiconductor …
Continue reading at www.spiedigitallibrary.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5068Physical circuit design, e.g. layout for integrated circuits or printed circuit boards
    • G06F17/5081Layout analysis, e.g. layout verification, design rule check
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition

Similar Documents

Publication Publication Date Title
Shin et al. Accurate lithography hotspot detection using deep convolutional neural networks
Rajaraman et al. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs
Yu et al. Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering
TW201734955A (en) Generate analog output for the sample
US12032892B2 (en) Semiconductor layout context around a point of interest
Lin et al. Machine learning for mask/wafer hotspot detection and mask synthesis
Sim et al. Automatic correction of lithography hotspots with a deep generative model
Matsunawa et al. Laplacian eigenmaps-and bayesian clustering-based layout pattern sampling and its applications to hotspot detection and optical proximity correction
Dehaerne et al. Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review
Xu et al. Lithography hotspot detection through multi-scale feature fusion utilizing feature pyramid network and dense block
Francisco et al. Multilayer CMP hotspot modeling through deep learning
Wang et al. Shape prior guided defect pattern classification and segmentation in wafer bin maps
Nosato et al. Hotspot prevention and detection method using an image-recognition technique based on higher-order local autocorrelation
Sun et al. Interpretable cnn-based lithographic hotspot detection through error marker learning
Patel et al. Engineering neural networks for improved defect detection and classification
Yan et al. Machine learning virtual SEM metrology and SEM-based OPC model methodology
Lu et al. Litho-neuralODE: improving hotspot detection accuracy with advanced data augmentation and neural ordinary differential equations
Yang et al. Hotspot detection using squish-net
Cheng et al. Deep learning hotspots detection with generative adversarial network-based data augmentation
Tseng et al. Advanced defect recognition on scanning electron microscope images: a two-stage strategy based on deep convolutional neural networks for hotspot monitoring
Borisov et al. Research on data augmentation for lithography hotspot detection using deep learning
Baranwal et al. Five deep learning recipes for the mask-making industry
Rana et al. Deep machine learning based Image classification in hard disk drive manufacturing
Gai et al. Sample patterns extraction from layout automatically based on hierarchical cluster algorithm for lithography process optimization
Hsieh et al. Recognition of defect spatial patterns in semiconductor fabrication