CN118297906B - 使用弱标记检测半导体试样中的缺陷 - Google Patents

使用弱标记检测半导体试样中的缺陷

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Publication number
CN118297906B
CN118297906B CN202410416642.3A CN202410416642A CN118297906B CN 118297906 B CN118297906 B CN 118297906B CN 202410416642 A CN202410416642 A CN 202410416642A CN 118297906 B CN118297906 B CN 118297906B
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CN118297906A (zh
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I·皮莱格
R·施莱恩
B·科恩
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Applied Materials Israel Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
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    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/9501—Semiconductor wafers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/22—Matching criteria, e.g. proximity measures
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    • G06N3/0464—Convolutional networks [CNN, ConvNet]
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-supervised learning
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    • G06N3/08—Learning methods
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    • G06T7/0008—Industrial image inspection checking presence/absence
    • G—PHYSICS
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    • G06T7/10—Segmentation; Edge detection
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
    • H01L22/10—Measuring as part of the manufacturing process
    • H01L22/12—Measuring as part of the manufacturing process for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions
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    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G06T2207/10061—Microscopic image from scanning electron microscope
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30148—Semiconductor; IC; Wafer

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CN202410416642.3A 2020-06-03 2021-04-26 使用弱标记检测半导体试样中的缺陷 Active CN118297906B (zh)

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US16/892,139 US11379972B2 (en) 2020-06-03 2020-06-03 Detecting defects in semiconductor specimens using weak labeling
US16/892,139 2020-06-03
CN202110455269.9A CN113763312B (zh) 2020-06-03 2021-04-26 使用弱标记检测半导体试样中的缺陷

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Families Citing this family (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11816411B2 (en) * 2020-01-29 2023-11-14 Taiwan Semiconductor Manufacturing Co., Ltd. Method and system for semiconductor wafer defect review
US11379972B2 (en) * 2020-06-03 2022-07-05 Applied Materials Israel Ltd. Detecting defects in semiconductor specimens using weak labeling
US11493901B2 (en) * 2020-09-24 2022-11-08 International Business Machines Corporation Detection of defect in edge device manufacturing by artificial intelligence
US20220101114A1 (en) * 2020-09-27 2022-03-31 Kla Corporation Interpretable deep learning-based defect detection and classification
US20230401821A1 (en) * 2020-11-09 2023-12-14 Canon U.S.A., Inc. Using activation maps to detect best areas of an image for prediction of noise levels
US12462074B2 (en) * 2021-01-22 2025-11-04 Nvidia Corporation Object simulation using real-world environments
US12430563B2 (en) * 2021-07-29 2025-09-30 GE Precision Healthcare LLC Learning-based clean data selection
CN115706819B (zh) * 2021-08-17 2024-12-31 鸿富锦精密工业（深圳）有限公司 网页视频播放方法、装置、电子设备及存储介质
TWI767828B (zh) * 2021-08-27 2022-06-11 開必拓數據股份有限公司 應用於具有卷對卷機構的設備的馬達控制系統
US20240013365A9 (en) * 2021-10-04 2024-01-11 Kla Corporation Unsupervised or self-supervised deep learning for semiconductor-based applications
TWI823159B (zh) * 2021-10-20 2023-11-21 開必拓數據股份有限公司 具人機互動功能的瑕疵檢測系統
US11789069B2 (en) * 2021-12-03 2023-10-17 International Business Machines Corporation Mixed high-resolution and low-resolution inspection for tamper detection
CN115061427B (zh) * 2022-06-28 2023-04-14 浙江同发塑机有限公司 吹塑机的料层均匀性控制系统及其控制方法
KR20240028665A (ko) * 2022-08-25 2024-03-05 한국전자통신연구원 딥러닝 서비스를 제공하기 위한 신경망 모델 배포 방법 및 장치
KR20240096217A (ko) * 2022-12-19 2024-06-26 삼성전자주식회사 제조 공정의 이상 여부를 판단하는 방법 및 장치
CN116046810B (zh) * 2023-04-03 2023-06-23 云南通衢工程检测有限公司 基于rpc盖板破坏荷载的无损检测方法
WO2025011912A1 (en) * 2023-07-13 2025-01-16 Asml Netherlands B.V. Systems and methods for defect inspection in charged-particle systems
KR102740270B1 (ko) * 2023-10-19 2024-12-10 주식회사 에이아이비즈 웨이퍼맵의 패턴을 분류하기 위한 모델을 학습시키는 방법, 장치 및 프로그램
US20250253121A1 (en) * 2024-02-07 2025-08-07 Kla Corporation Systems and methods of sem inspection using selective scan approach

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110945528A (zh) * 2018-02-07 2020-03-31 应用材料以色列公司 产生可用于检查半导体样品的训练集的方法及其系统

Family Cites Families (44)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3534582B2 (ja) * 1997-10-02 2004-06-07 株式会社日立製作所 パターン欠陥検査方法および検査装置
US7570796B2 (en) * 2005-11-18 2009-08-04 Kla-Tencor Technologies Corp. Methods and systems for utilizing design data in combination with inspection data
US7676077B2 (en) * 2005-11-18 2010-03-09 Kla-Tencor Technologies Corp. Methods and systems for utilizing design data in combination with inspection data
JP4928862B2 (ja) * 2006-08-04 2012-05-09 株式会社日立ハイテクノロジーズ 欠陥検査方法及びその装置
JP5065943B2 (ja) * 2008-02-29 2012-11-07 株式会社日立ハイテクノロジーズ 製造プロセスモニタリングシステム
US8150140B2 (en) * 2008-12-22 2012-04-03 Ngr Inc. System and method for a semiconductor lithographic process control using statistical information in defect identification
JP5429869B2 (ja) * 2008-12-22 2014-02-26 株式会社 Ｎｇｒ パターン検査装置および方法
US9390490B2 (en) * 2010-01-05 2016-07-12 Hitachi High-Technologies Corporation Method and device for testing defect using SEM
US8669523B2 (en) * 2011-05-25 2014-03-11 Kla-Tencor Corporation Contour-based defect detection using an inspection apparatus
KR102019534B1 (ko) * 2013-02-01 2019-09-09 케이엘에이 코포레이션 결함 특유의, 다중 채널 정보를 이용한 웨이퍼 상의 결함 검출
US9171364B2 (en) * 2013-06-21 2015-10-27 Kla-Tencor Corp. Wafer inspection using free-form care areas
US9714905B1 (en) * 2013-06-23 2017-07-25 Kla-Tencor Corp. Wafer inspection recipe setup
US9355208B2 (en) * 2013-07-08 2016-05-31 Kla-Tencor Corp. Detecting defects on a wafer
US9518935B2 (en) * 2013-07-29 2016-12-13 Kla-Tencor Corporation Monitoring changes in photomask defectivity
US10410338B2 (en) * 2013-11-04 2019-09-10 Kla-Tencor Corporation Method and system for correlating optical images with scanning electron microscopy images
US9613411B2 (en) * 2014-03-17 2017-04-04 Kla-Tencor Corp. Creating defect classifiers and nuisance filters
JP2016058465A (ja) * 2014-09-08 2016-04-21 株式会社日立ハイテクノロジーズ 欠陥定量化方法、欠陥定量化装置、および欠陥評価値表示装置
US9846934B2 (en) 2015-04-13 2017-12-19 Anchor Semiconductor Inc. Pattern weakness and strength detection and tracking during a semiconductor device fabrication process
US9767548B2 (en) * 2015-04-24 2017-09-19 Kla-Tencor Corp. Outlier detection on pattern of interest image populations
US10359371B2 (en) * 2015-08-24 2019-07-23 Kla-Tencor Corp. Determining one or more characteristics of a pattern of interest on a specimen
US11205119B2 (en) * 2015-12-22 2021-12-21 Applied Materials Israel Ltd. Method of deep learning-based examination of a semiconductor specimen and system thereof
US9916965B2 (en) * 2015-12-31 2018-03-13 Kla-Tencor Corp. Hybrid inspectors
US10181185B2 (en) * 2016-01-11 2019-01-15 Kla-Tencor Corp. Image based specimen process control
US11010886B2 (en) * 2016-05-17 2021-05-18 Kla-Tencor Corporation Systems and methods for automatic correction of drift between inspection and design for massive pattern searching
US10706522B2 (en) * 2016-11-08 2020-07-07 Kla-Tencor Corporation System and method for generation of wafer inspection critical areas
US10451563B2 (en) * 2017-02-21 2019-10-22 Kla-Tencor Corporation Inspection of photomasks by comparing two photomasks
US10620135B2 (en) * 2017-07-19 2020-04-14 Kla-Tencor Corp. Identifying a source of nuisance defects on a wafer
US9964607B1 (en) * 2017-08-28 2018-05-08 Seagate Technology Llc Recognizing and identifying defect patterns on magnetic media
US10607119B2 (en) * 2017-09-06 2020-03-31 Kla-Tencor Corp. Unified neural network for defect detection and classification
US11138507B2 (en) * 2017-09-28 2021-10-05 Applied Materials Israel Ltd. System, method and computer program product for classifying a multiplicity of items
WO2019070600A1 (en) * 2017-10-02 2019-04-11 Applied Materials Israel Ltd. DETERMINING A CRITICAL DIMENSION VARIATION OF A PATTERN
US10423861B2 (en) * 2017-10-16 2019-09-24 Illumina, Inc. Deep learning-based techniques for training deep convolutional neural networks
KR20190073756A (ko) * 2017-12-19 2019-06-27 삼성전자주식회사 반도체 결함 분류 장치, 반도체의 결함을 분류하는 방법, 그리고 반도체 결함 분류 시스템
WO2019155471A1 (en) * 2018-02-07 2019-08-15 Applied Materials Israel Ltd. Method of deep learning-based examination of a semiconductor specimen and system thereof
US11199506B2 (en) * 2018-02-21 2021-12-14 Applied Materials Israel Ltd. Generating a training set usable for examination of a semiconductor specimen
US10599951B2 (en) * 2018-03-28 2020-03-24 Kla-Tencor Corp. Training a neural network for defect detection in low resolution images
WO2020043525A1 (en) * 2018-08-28 2020-03-05 Asml Netherlands B.V. Systems and methods of optimal metrology guidance
US10825650B2 (en) 2018-09-28 2020-11-03 Taiwan Semiconductor Manufacturing Co., Ltd. Machine learning on wafer defect review
KR102649132B1 (ko) * 2018-12-31 2024-03-20 에이에스엠엘 네델란즈 비.브이. E-빔 이미지 향상을 위한 완전 자동화 sem 샘플링 시스템
US10957034B2 (en) * 2019-01-17 2021-03-23 Applied Materials Israel Ltd. Method of examination of a specimen and system thereof
CN109978764B (zh) * 2019-03-11 2021-03-02 厦门美图之家科技有限公司 一种图像处理方法及计算设备
US11151710B1 (en) * 2020-05-04 2021-10-19 Applied Materials Israel Ltd. Automatic selection of algorithmic modules for examination of a specimen
US11379972B2 (en) * 2020-06-03 2022-07-05 Applied Materials Israel Ltd. Detecting defects in semiconductor specimens using weak labeling
US20220044949A1 (en) * 2020-08-06 2022-02-10 Carl Zeiss Smt Gmbh Interactive and iterative training of a classification algorithm for classifying anomalies in imaging datasets

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110945528A (zh) * 2018-02-07 2020-03-31 应用材料以色列公司 产生可用于检查半导体样品的训练集的方法及其系统

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CN113763312B (zh) 2024-04-19
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JP2021190716A (ja) 2021-12-13
US11379972B2 (en) 2022-07-05
US20210383530A1 (en) 2021-12-09
CN113763312A (zh) 2021-12-07
JP7633094B2 (ja) 2025-02-19
TWI864281B (zh) 2024-12-01
CN118297906A (zh) 2024-07-05
US20220301151A1 (en) 2022-09-22
US11790515B2 (en) 2023-10-17

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