JP2022504505A5 - - Google Patents
Info
- Publication number
- JP2022504505A5 JP2022504505A5 JP2021519555A JP2021519555A JP2022504505A5 JP 2022504505 A5 JP2022504505 A5 JP 2022504505A5 JP 2021519555 A JP2021519555 A JP 2021519555A JP 2021519555 A JP2021519555 A JP 2021519555A JP 2022504505 A5 JP2022504505 A5 JP 2022504505A5
- Authority
- JP
- Japan
- Prior art keywords
- learning classifier
- interest
- machine learning
- training
- product
- 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.)
- Granted
Links
Applications Claiming Priority (7)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| IN201841037993 | 2018-10-08 | ||
| IN201841037993 | 2018-10-08 | ||
| US201862770712P | 2018-11-21 | 2018-11-21 | |
| US62/770,712 | 2018-11-21 | ||
| US16/420,408 US11094053B2 (en) | 2018-10-08 | 2019-05-23 | Deep learning based adaptive regions of interest for critical dimension measurements of semiconductor substrates |
| US16/420,408 | 2019-05-23 | ||
| PCT/US2019/053922 WO2020076544A1 (en) | 2018-10-08 | 2019-10-01 | Deep learning based adaptive regions of interest for critical dimension measurements of semiconductor substrates |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2022504505A JP2022504505A (ja) | 2022-01-13 |
| JP2022504505A5 true JP2022504505A5 (enExample) | 2022-10-06 |
| JP7284813B2 JP7284813B2 (ja) | 2023-05-31 |
Family
ID=70052261
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2021519555A Active JP7284813B2 (ja) | 2018-10-08 | 2019-10-01 | 半導体基板の限界寸法測定のための深層学習ベースの適応関心領域 |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US11094053B2 (enExample) |
| EP (1) | EP3853885B1 (enExample) |
| JP (1) | JP7284813B2 (enExample) |
| KR (1) | KR102576880B1 (enExample) |
| CN (1) | CN112823412B (enExample) |
| TW (1) | TWI808265B (enExample) |
| WO (1) | WO2020076544A1 (enExample) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2021026926A (ja) * | 2019-08-07 | 2021-02-22 | 株式会社日立ハイテク | 画像生成方法、非一時的コンピューター可読媒体、及びシステム |
| CN116880134A (zh) * | 2020-01-07 | 2023-10-13 | 诺威有限公司 | 用于ocd计量的方法 |
| CN113809117B (zh) * | 2020-06-16 | 2023-12-22 | 联华电子股份有限公司 | 半导体元件及其制作方法 |
| US11967058B2 (en) | 2020-06-24 | 2024-04-23 | Kla Corporation | Semiconductor overlay measurements using machine learning |
| CN113393447B (zh) * | 2021-06-24 | 2022-08-02 | 四川启睿克科技有限公司 | 基于深度学习的针尖正位度检测方法及系统 |
| US12223752B2 (en) | 2021-09-30 | 2025-02-11 | Fei Company | Data acquisition in charged particle microscopy |
| WO2024049199A1 (ko) * | 2022-08-31 | 2024-03-07 | 주식회사 엘지에너지솔루션 | 학습 모델 기반의 치수 측정 장치 및 방법 |
| WO2024065645A1 (zh) * | 2022-09-30 | 2024-04-04 | 北京京东方技术开发有限公司 | 图像文本匹配模型的训练方法、装置、设备及存储介质 |
| US20240161264A1 (en) * | 2022-11-15 | 2024-05-16 | Micron Technology, Inc. | Defect characterization in semiconductor devices based on image processing |
| US12449379B2 (en) | 2023-05-25 | 2025-10-21 | Applied Materials, Inc. | Machine learning model training |
| US20240394509A1 (en) * | 2023-05-25 | 2024-11-28 | Applied Materials, Inc. | Generating synthetic microspy images of substrates |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5238465B2 (ja) | 2008-11-25 | 2013-07-17 | 株式会社東芝 | パターン形状の評価方法及びこれを利用したパターン形状の評価装置 |
| US9189844B2 (en) | 2012-10-15 | 2015-11-17 | Kla-Tencor Corp. | Detecting defects on a wafer using defect-specific information |
| US9490182B2 (en) * | 2013-12-23 | 2016-11-08 | Kla-Tencor Corporation | Measurement of multiple patterning parameters |
| US9087176B1 (en) * | 2014-03-06 | 2015-07-21 | Kla-Tencor Corporation | Statistical overlay error prediction for feed forward and feedback correction of overlay errors, root cause analysis and process control |
| US10127653B2 (en) | 2014-07-22 | 2018-11-13 | Kla-Tencor Corp. | Determining coordinates for an area of interest on a specimen |
| KR102238742B1 (ko) | 2014-09-11 | 2021-04-12 | 삼성전자주식회사 | 마스크 패턴의 측정 관심 영역 그룹화 방법 및 이를 이용한 마스크 패턴의 선폭 계측 방법 |
| US10483081B2 (en) | 2014-10-22 | 2019-11-19 | Kla-Tencor Corp. | Self directed metrology and pattern classification |
| US10267746B2 (en) * | 2014-10-22 | 2019-04-23 | Kla-Tencor Corp. | Automated pattern fidelity measurement plan generation |
| US9898811B2 (en) * | 2015-05-08 | 2018-02-20 | Kla-Tencor Corporation | Method and system for defect classification |
| TWI684225B (zh) * | 2015-08-28 | 2020-02-01 | 美商克萊譚克公司 | 自定向計量和圖樣分類 |
| US9875534B2 (en) * | 2015-09-04 | 2018-01-23 | Kla-Tencor Corporation | Techniques and systems for model-based critical dimension measurements |
| SG10201912510QA (en) * | 2015-09-23 | 2020-02-27 | Kla Tencor Corp | Method and system for focus adjustment a multi-beam scanning electron microscopy system |
| US10181185B2 (en) * | 2016-01-11 | 2019-01-15 | Kla-Tencor Corp. | Image based specimen process control |
| CN107305636A (zh) * | 2016-04-22 | 2017-10-31 | 株式会社日立制作所 | 目标识别方法、目标识别装置、终端设备和目标识别系统 |
| WO2017200524A1 (en) * | 2016-05-16 | 2017-11-23 | United Technologies Corporation | Deep convolutional neural networks for crack detection from image data |
| KR102606308B1 (ko) | 2016-06-28 | 2023-11-24 | 삼성전자주식회사 | 포토 마스크의 제조 방법, 패턴 형성 방법 및 반도체 장치의 제조 방법 |
| US10402688B2 (en) * | 2016-12-07 | 2019-09-03 | Kla-Tencor Corporation | Data augmentation for convolutional neural network-based defect inspection |
| US10565702B2 (en) * | 2017-01-30 | 2020-02-18 | Dongfang Jingyuan Electron Limited | Dynamic updates for the inspection of integrated circuits |
-
2019
- 2019-05-23 US US16/420,408 patent/US11094053B2/en active Active
- 2019-10-01 WO PCT/US2019/053922 patent/WO2020076544A1/en not_active Ceased
- 2019-10-01 JP JP2021519555A patent/JP7284813B2/ja active Active
- 2019-10-01 CN CN201980065799.3A patent/CN112823412B/zh active Active
- 2019-10-01 EP EP19871388.5A patent/EP3853885B1/en active Active
- 2019-10-01 KR KR1020217013707A patent/KR102576880B1/ko active Active
- 2019-10-07 TW TW108136245A patent/TWI808265B/zh active
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2022504505A5 (enExample) | ||
| US20150279615A1 (en) | Imaging a Sample with Multiple Beams and Multiple Detectors | |
| JP7284813B2 (ja) | 半導体基板の限界寸法測定のための深層学習ベースの適応関心領域 | |
| JP7587649B2 (ja) | テレセントリック照明を有するマルチビーム電子特性評価ツール | |
| US11545336B2 (en) | Scanning electron microscopy system and pattern depth measurement method | |
| JP2019129072A (ja) | 走査電子顕微鏡および測定方法 | |
| TW202001185A (zh) | 圖案測定方法、圖案測定工具、及電腦可讀媒體 | |
| US12211668B2 (en) | Charged particle beam device | |
| JP7323574B2 (ja) | 荷電粒子線装置および画像取得方法 | |
| US10190875B2 (en) | Pattern measurement condition setting device and pattern measuring device | |
| US20210366685A1 (en) | Charged-Particle Beam Device and Cross-Sectional Shape Estimation Program | |
| TW202111318A (zh) | 基板的結構的狀態的基於x 射線的評估 | |
| US12400383B2 (en) | Training method for learning apparatus, and image generation system | |
| US20190362935A1 (en) | Reflection-Mode Electron-Beam Inspection Using Ptychographic Imaging | |
| US9184034B2 (en) | Photomultiplier tube with extended dynamic range | |
| KR20210010514A (ko) | 전자 빔 유도 전류에 기초한 웨이퍼 검사 | |
| JP2020060381A (ja) | 元素マップの生成方法および表面分析装置 | |
| EP4332878A1 (en) | Optical image processing method, machine learning method, trained model, machine learning preprocessing method, optical image processing module, optical image processing program, and optical image processing system | |
| KR102700926B1 (ko) | 하전 입자선 장치 | |
| JP6660774B2 (ja) | 高さデータ処理装置、表面形状測定装置、高さデータ補正方法、及びプログラム | |
| US10957513B2 (en) | Electron microscope and image processing method | |
| US12597584B2 (en) | Charged particle beam apparatus and processor system | |
| US20260036535A1 (en) | System and method for scanning electron beam image-formation with elemental analysis | |
| JP7177088B2 (ja) | 粒子形状分析方法、顕微鏡および顕微鏡システム | |
| KR20240105259A (ko) | 프로세서 시스템, 보정 방법 및 보정 프로그램 |