JP7633094B2 - 弱いラベル付けを使用した半導体試料内の欠陥の検出 - Google Patents
弱いラベル付けを使用した半導体試料内の欠陥の検出 Download PDFInfo
- Publication number
- JP7633094B2 JP7633094B2 JP2021093501A JP2021093501A JP7633094B2 JP 7633094 B2 JP7633094 B2 JP 7633094B2 JP 2021093501 A JP2021093501 A JP 2021093501A JP 2021093501 A JP2021093501 A JP 2021093501A JP 7633094 B2 JP7633094 B2 JP 7633094B2
- Authority
- JP
- Japan
- Prior art keywords
- training
- poi
- pixel block
- neural network
- feature map
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
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)
- Pathology (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Neurology (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Computer Hardware Design (AREA)
- Power Engineering (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)
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 (3)
| Publication Number | Publication Date |
|---|---|
| JP2021190716A JP2021190716A (ja) | 2021-12-13 |
| JP2021190716A5 JP2021190716A5 (enExample) | 2024-06-10 |
| JP7633094B2 true JP7633094B2 (ja) | 2025-02-19 |
Family
ID=78786923
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2021093501A Active JP7633094B2 (ja) | 2020-06-03 | 2021-06-03 | 弱いラベル付けを使用した半導体試料内の欠陥の検出 |
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)
| 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 (3)
| 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 |
| WO2019050992A1 (en) | 2017-09-06 | 2019-03-14 | Kla-Tencor Corporation | UNIFIED NEURONAL NETWORK FOR DETECTION AND CLASSIFICATION OF DEFECTS |
| US20200105500A1 (en) | 2018-09-28 | 2020-04-02 | Taiwan Semiconductor Manufacturing Co., Ltd. | Machine learning on wafer defect review |
Family Cites Families (42)
| 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 |
| 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 |
| 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 |
| KR102176335B1 (ko) * | 2018-02-07 | 2020-11-10 | 어플라이드 머티리얼즈 이스라엘 리미티드 | 반도체 시편의 시험을 위해 사용 가능한 훈련 세트를 생성하는 방법 및 그 시스템 |
| 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 |
| 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 |
-
2020
- 2020-06-03 US US16/892,139 patent/US11379972B2/en active Active
-
2021
- 2021-04-16 KR KR1020210049665A patent/KR102749767B1/ko active Active
- 2021-04-26 CN CN202110455269.9A patent/CN113763312B/zh active Active
- 2021-04-26 CN CN202410416642.3A patent/CN118297906B/zh active Active
- 2021-04-26 TW TW110114852A patent/TWI864281B/zh active
- 2021-06-03 JP JP2021093501A patent/JP7633094B2/ja active Active
-
2022
- 2022-05-23 US US17/751,507 patent/US11790515B2/en active Active
Patent Citations (3)
| 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 |
| WO2019050992A1 (en) | 2017-09-06 | 2019-03-14 | Kla-Tencor Corporation | UNIFIED NEURONAL NETWORK FOR DETECTION AND CLASSIFICATION OF DEFECTS |
| US20200105500A1 (en) | 2018-09-28 | 2020-04-02 | Taiwan Semiconductor Manufacturing Co., Ltd. | Machine learning on wafer defect review |
Also Published As
| Publication number | Publication date |
|---|---|
| KR20210150970A (ko) | 2021-12-13 |
| KR102749767B1 (ko) | 2025-01-07 |
| CN113763312B (zh) | 2024-04-19 |
| TW202147163A (zh) | 2021-12-16 |
| JP2021190716A (ja) | 2021-12-13 |
| CN118297906B (zh) | 2025-07-29 |
| US11379972B2 (en) | 2022-07-05 |
| US20210383530A1 (en) | 2021-12-09 |
| CN113763312A (zh) | 2021-12-07 |
| TWI864281B (zh) | 2024-12-01 |
| CN118297906A (zh) | 2024-07-05 |
| US20220301151A1 (en) | 2022-09-22 |
| US11790515B2 (en) | 2023-10-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7633094B2 (ja) | 弱いラベル付けを使用した半導体試料内の欠陥の検出 | |
| CN113269709B (zh) | 探测半导体晶片中缺陷的方法及半导体晶片缺陷探测系统 | |
| CN111512324B (zh) | 半导体样品的基于深度学习的检查的方法及其系统 | |
| TWI767108B (zh) | 用於檢查半導體試樣的方法與系統及在其上紀錄相關指令的電腦可讀取媒體 | |
| US20220067523A1 (en) | Method of deep learining-based examination of a semiconductor specimen and system thereof | |
| 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 | |
| JP2021190716A5 (enExample) | ||
| KR20200014927A (ko) | 반도체 시편의 시험을 위해 사용 가능한 훈련 세트를 생성하는 방법 및 그 시스템 | |
| TWI786570B (zh) | 生成可用於檢查半導體樣本的訓練集 | |
| KR20150140349A (ko) | 반도체 검사 레시피 생성, 결함 리뷰 및 계측을 위한 적응적 샘플링 | |
| CN110648310A (zh) | 基于注意力机制的弱监督铸件缺陷识别方法 | |
| US11151710B1 (en) | Automatic selection of algorithmic modules for examination of a specimen | |
| TW202339038A (zh) | 基於機器學習的半導體樣品的檢查及其訓練 | |
| Li et al. | A defect detection method based on improved mask R-CNN for wafer maps | |
| US12480889B2 (en) | Inspection recipe optimization for semiconductor specimens | |
| CN115953641A (zh) | 半导体样本的缺陷检查 | |
| Danajitha et al. | Detection of cracks in high rise buildings using drones | |
| CN119168939A (zh) | 用于半导体样本的基于机器学习的缺陷检查 | |
| CN117529803A (zh) | 用于主动良率管理的制造指纹 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20240531 |
|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20240531 |
|
| A871 | Explanation of circumstances concerning accelerated examination |
Free format text: JAPANESE INTERMEDIATE CODE: A871 Effective date: 20240531 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20240829 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20241129 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20250107 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20250206 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 7633094 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |