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

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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
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依瑞 帕雷
拉恩 斯柯勒言
寶茲 扣漢
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以色列商應用材料以色列公司
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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)

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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

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US (2) US11379972B2 (https=)
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Families Citing this family (20)

* 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
US12567233B2 (en) * 2020-11-09 2026-03-03 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
JP2024082013A (ja) * 2022-12-07 2024-06-19 株式会社Ｓｃｒｅｅｎホールディングス 画像処理装置、特徴抽出器の学習方法、識別器の更新方法、および画像処理方法
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
KR101342203B1 (ko) * 2010-01-05 2013-12-16 가부시키가이샤 히다치 하이테크놀로지즈 Ｓｅｍ을 이용한 결함 검사 방법 및 장치
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
KR102611427B1 (ko) * 2017-10-02 2023-12-07 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
KR102549196B1 (ko) * 2018-02-07 2023-06-30 어플라이드 머티리얼즈 이스라엘 리미티드 반도체 시편의 심층 학습 기반 검사 방법 및 그의 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
US10825650B2 (en) * 2018-09-28 2020-11-03 Taiwan Semiconductor Manufacturing Co., Ltd. Machine learning on wafer defect review
KR20240038156A (ko) * 2018-12-31 2024-03-22 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 (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

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