TWI864281B - 對半導體試樣上的一感興趣圖案（ｐｏｉ）進行分類的系統與方法，及電腦程式產品執行訓練一機器學習模型的方法以生成可用於對ｐｏｉ進行分類的資料 - Google Patents

對半導體試樣上的一感興趣圖案（ｐｏｉ）進行分類的系統與方法，及電腦程式產品執行訓練一機器學習模型的方法以生成可用於對ｐｏｉ進行分類的資料 Download PDF

Info

Publication number
TWI864281B
TWI864281B TW110114852A TW110114852A TWI864281B TW I864281 B TWI864281 B TW I864281B TW 110114852 A TW110114852 A TW 110114852A TW 110114852 A TW110114852 A TW 110114852A TW I864281 B TWI864281 B TW I864281B
Authority
TW
Taiwan
Prior art keywords
poi
training
neural network
resolution
per
Prior art date
Application number
TW110114852A
Other languages
English (en)
Chinese (zh)
Other versions
TW202147163A (zh
Inventor
依瑞 帕雷
拉恩 斯柯勒言
寶茲 扣漢
Original Assignee
以色列商應用材料以色列公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 以色列商應用材料以色列公司 filed Critical 以色列商應用材料以色列公司
Publication of TW202147163A publication Critical patent/TW202147163A/zh
Application granted granted Critical
Publication of TWI864281B publication Critical patent/TWI864281B/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/0008—Industrial image inspection checking presence/absence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • 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
    • G06T2207/10—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

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Multimedia (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Databases & Information Systems (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Manufacturing & Machinery (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Neurology (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Power Engineering (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Computer Hardware Design (AREA)
  • Image Analysis (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)
TW110114852A 2020-06-03 2021-04-26 對半導體試樣上的一感興趣圖案（ｐｏｉ）進行分類的系統與方法，及電腦程式產品執行訓練一機器學習模型的方法以生成可用於對ｐｏｉ進行分類的資料 TWI864281B (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
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

Publications (2)

Publication Number Publication Date
TW202147163A TW202147163A (zh) 2021-12-16
TWI864281B true TWI864281B (zh) 2024-12-01

Family

ID=78786923

Family Applications (1)

Application Number Title Priority Date Filing Date
TW110114852A TWI864281B (zh) 2020-06-03 2021-04-26 對半導體試樣上的一感興趣圖案（ｐｏｉ）進行分類的系統與方法，及電腦程式產品執行訓練一機器學習模型的方法以生成可用於對ｐｏｉ進行分類的資料

Country Status (5)

Country Link
US (2) US11379972B2 (enExample)
JP (1) JP7633094B2 (enExample)
KR (1) KR102749767B1 (enExample)
CN (2) CN113763312B (enExample)
TW (1) TWI864281B (enExample)

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
WO2022099133A1 (en) * 2020-11-09 2022-05-12 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 (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160300338A1 (en) * 2015-04-13 2016-10-13 Anchor Semiconductor Inc. Pattern weakness and strength detection and tracking during a semiconductor device fabrication process
TW201732690A (zh) * 2015-12-22 2017-09-16 應用材料以色列公司 半導體試樣的基於深度學習之檢查的方法及其系統
US9964607B1 (en) * 2017-08-28 2018-05-08 Seagate Technology Llc Recognizing and identifying defect patterns on magnetic media
CN110870019A (zh) * 2017-10-16 2020-03-06 因美纳有限公司 用于训练深层卷积神经网络集合的半监督学习

Family Cites Families (41)

* 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
US9092846B2 (en) * 2013-02-01 2015-07-28 Kla-Tencor Corp. Detecting defects on a wafer using defect-specific and multi-channel information
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
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
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
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
KR102810067B1 (ko) * 2017-10-02 2025-05-21 어플라이드 머티리얼즈 이스라엘 리미티드 패턴의 임계 치수 변동의 결정
KR20190073756A (ko) * 2017-12-19 2019-06-27 삼성전자주식회사 반도체 결함 분류 장치, 반도체의 결함을 분류하는 방법, 그리고 반도체 결함 분류 시스템
CN110945528B (zh) * 2018-02-07 2021-04-02 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
CN111512324B (zh) * 2018-02-07 2024-06-28 应用材料以色列公司 半导体样品的基于深度学习的检查的方法及其系统
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
KR20210036962A (ko) * 2018-08-28 2021-04-05 에이에스엠엘 네델란즈 비.브이. 최적의 계측 안내 시스템들 및 방법들
US10825650B2 (en) * 2018-09-28 2020-11-03 Taiwan Semiconductor Manufacturing Co., Ltd. Machine learning on wafer defect review
CN113272820B (zh) * 2018-12-31 2025-11-28 Asml荷兰有限公司 用于电子束图像增强的全自动sem采样系统
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 (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160300338A1 (en) * 2015-04-13 2016-10-13 Anchor Semiconductor Inc. Pattern weakness and strength detection and tracking during a semiconductor device fabrication process
TW201732690A (zh) * 2015-12-22 2017-09-16 應用材料以色列公司 半導體試樣的基於深度學習之檢查的方法及其系統
US9964607B1 (en) * 2017-08-28 2018-05-08 Seagate Technology Llc Recognizing and identifying defect patterns on magnetic media
CN110870019A (zh) * 2017-10-16 2020-03-06 因美纳有限公司 用于训练深层卷积神经网络集合的半监督学习

Also Published As

Publication number Publication date
CN113763312B (zh) 2024-04-19
CN118297906B (zh) 2025-07-29
KR102749767B1 (ko) 2025-01-07
US11790515B2 (en) 2023-10-17
US20210383530A1 (en) 2021-12-09
TW202147163A (zh) 2021-12-16
JP7633094B2 (ja) 2025-02-19
CN113763312A (zh) 2021-12-07
US11379972B2 (en) 2022-07-05
JP2021190716A (ja) 2021-12-13
KR20210150970A (ko) 2021-12-13
CN118297906A (zh) 2024-07-05
US20220301151A1 (en) 2022-09-22

Similar Documents

Publication Publication Date Title
TWI864281B (zh) 對半導體試樣上的一感興趣圖案（ｐｏｉ）進行分類的系統與方法，及電腦程式產品執行訓練一機器學習模型的方法以生成可用於對ｐｏｉ進行分類的資料
CN111512324B (zh) 半导体样品的基于深度学习的检查的方法及其系统
CN114092387B (zh) 生成可用于检查半导体样本的训练数据
TWI889675B (zh) 基於機器學習之半導體樣本中的缺陷分類之方法、系統及非暫態電腦可讀取媒體
TW202127370A (zh) 基於機器學習的取樣缺陷檢測
KR20150140349A (ko) 반도체 검사 레시피 생성, 결함 리뷰 및 계측을 위한 적응적 샘플링
TWI864314B (zh) 樣本的類比圖像的決定
TW202339038A (zh) 基於機器學習的半導體樣品的檢查及其訓練
TWI874666B (zh) 在半導體樣本中的陣列的識別
US20230408423A1 (en) Inspection recipe optimization for semiconductor specimens
TW202524073A (zh) 半導體取樣的基於機器學習的缺陷檢查和排名
CN117529803A (zh) 用于主动良率管理的制造指纹