[go: up one dir, main page]

Sim et al., 2019 - Google Patents

Automatic correction of lithography hotspots with a deep generative model

Sim et al., 2019

View PDF
Document ID
11614693350766470895
Author
Sim W
Lee K
Yang D
Jeong J
Hong J
Lee S
Lee H
Publication year
Publication venue
Optical Microlithography XXXII

External Links

Snippet

Deep learning has recently been successfully applied to lithography hotspot detection. However, automatic correction of the detected hotspots into non-hotspots has not been explored. This problem is challenging because the standard supervised learning requires a …
Continue reading at web.eecs.umich.edu (PDF) (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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • 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
    • 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
    • G06F17/20Handling natural language data
    • 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
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G03PHOTOGRAPHY; CINEMATOGRAPHY; ELECTROGRAPHY; HOLOGRAPHY
    • G03FPHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F1/00Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof

Similar Documents

Publication Publication Date Title
Sengar et al. Generative artificial intelligence: a systematic review and applications
Shin et al. Accurate lithography hotspot detection using deep convolutional neural networks
Sim et al. Automatic correction of lithography hotspots with a deep generative model
Cheng et al. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks
CN105718952A (en) Method for focus classification of sectional medical images by employing deep learning network
Tan et al. Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function
Awad et al. Accurate prediction of EUV lithographic images and 3D mask effects using generative networks
Hering et al. Unsupervised learning for large motion thoracic CT follow-up registration
Zhu et al. New loss functions for medical image registration based on VoxelMorph
Ciou et al. SRAF placement with generative adversarial network
Xu et al. Lithography hotspot detection through multi-scale feature fusion utilizing feature pyramid network and dense block
Ciou et al. Machine learning optical proximity correction with generative adversarial networks
Selvam et al. Deep learning-based hotspot prediction of via printability in process window corners
Ciou et al. Machine learning OPC with generative adversarial networks
Choi et al. Fast and accurate automatic wafer defect detection and classification using machine learning based SEM image analysis
Dutta et al. Impact of data augmentation techniques on a deep learning based medical imaging task
He et al. Effective 3D humerus and scapula extraction using low-contrast and high-shape-variability MR data
Cao et al. Curvilinear mask optimization with refined generative adversarial nets
Xu et al. SwinT-ILT: Swin Transformer embedding end-to-end mask optimization model
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
Gutierrez et al. Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection
Lee et al. Deep learning-based detection of mask rule check violations in curvilinear mask
He et al. Semi-automatic initialization of gradient vector flow snakes
Moradi et al. Quality controlled segmentation to aid disease detection