JP6853273B2 - ニューラルネットワークと順物理モデルを半導体用途に組み込んだシステムおよび方法 - Google Patents
ニューラルネットワークと順物理モデルを半導体用途に組み込んだシステムおよび方法 Download PDFInfo
- Publication number
- JP6853273B2 JP6853273B2 JP2018563511A JP2018563511A JP6853273B2 JP 6853273 B2 JP6853273 B2 JP 6853273B2 JP 2018563511 A JP2018563511 A JP 2018563511A JP 2018563511 A JP2018563511 A JP 2018563511A JP 6853273 B2 JP6853273 B2 JP 6853273B2
- Authority
- JP
- Japan
- Prior art keywords
- image
- neural network
- sample
- input
- inverse
- 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
- 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
- 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
- G06N3/0499—Feedforward networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
-
- 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
-
- 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
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0475—Generative 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
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
-
- 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
- G06N3/088—Non-supervised learning, e.g. competitive 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
- G06N3/0895—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
- G06N3/094—Adversarial learning
-
- 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
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Medical Informatics (AREA)
- Databases & Information Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Neurology (AREA)
- Testing Or Measuring Of Semiconductors Or The Like (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Length Measuring Devices By Optical Means (AREA)
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201662344214P | 2016-06-01 | 2016-06-01 | |
| US62/344,214 | 2016-06-01 | ||
| US15/609,009 | 2017-05-31 | ||
| US15/609,009 US10346740B2 (en) | 2016-06-01 | 2017-05-31 | Systems and methods incorporating a neural network and a forward physical model for semiconductor applications |
| PCT/US2017/035494 WO2017210455A1 (en) | 2016-06-01 | 2017-06-01 | Systems and methods incorporating a neural network and a forward physical model for semiconductor applications |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2019525450A JP2019525450A (ja) | 2019-09-05 |
| JP2019525450A5 JP2019525450A5 (enExample) | 2020-07-30 |
| JP6853273B2 true JP6853273B2 (ja) | 2021-03-31 |
Family
ID=60479143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2018563511A Active JP6853273B2 (ja) | 2016-06-01 | 2017-06-01 | ニューラルネットワークと順物理モデルを半導体用途に組み込んだシステムおよび方法 |
Country Status (8)
| Country | Link |
|---|---|
| US (1) | US10346740B2 (enExample) |
| EP (1) | EP3465552B1 (enExample) |
| JP (1) | JP6853273B2 (enExample) |
| KR (1) | KR102213730B1 (enExample) |
| CN (1) | CN109313724B (enExample) |
| IL (1) | IL262787B (enExample) |
| TW (1) | TWI715773B (enExample) |
| WO (1) | WO2017210455A1 (enExample) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220121956A1 (en) * | 2020-10-16 | 2022-04-21 | Samsung Electronics Co., Ltd. | Method of training deep learning model for predicting pattern characteristics and method of manufacturing semiconductor device |
Families Citing this family (133)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10373055B1 (en) * | 2016-05-20 | 2019-08-06 | Deepmind Technologies Limited | Training variational autoencoders to generate disentangled latent factors |
| US10181391B2 (en) * | 2016-05-26 | 2019-01-15 | Nanojehm Inc. | Image processing system and method of processing images |
| US10197908B2 (en) | 2016-06-21 | 2019-02-05 | Lam Research Corporation | Photoresist design layout pattern proximity correction through fast edge placement error prediction via a physics-based etch profile modeling framework |
| US10504004B2 (en) * | 2016-09-16 | 2019-12-10 | General Dynamics Mission Systems, Inc. | Systems and methods for deep model translation generation |
| DE112016007498B4 (de) * | 2016-12-06 | 2020-11-26 | Mitsubishi Electric Corporation | Untersuchungseinrichtung und untersuchungsverfahren |
| US11237872B2 (en) | 2017-05-23 | 2022-02-01 | Kla-Tencor Corporation | Semiconductor inspection and metrology systems for distributing job among the CPUs or GPUs based on logical image processing boundaries |
| US11282695B2 (en) * | 2017-09-26 | 2022-03-22 | Samsung Electronics Co., Ltd. | Systems and methods for wafer map analysis |
| TWI653605B (zh) | 2017-12-25 | 2019-03-11 | 由田新技股份有限公司 | 利用深度學習的自動光學檢測方法、設備、電腦程式、電腦可讀取之記錄媒體及其深度學習系統 |
| US20210271968A1 (en) * | 2018-02-09 | 2021-09-02 | Deepmind Technologies Limited | Generative neural network systems for generating instruction sequences to control an agent performing a task |
| EP3531205A1 (en) | 2018-02-22 | 2019-08-28 | ASML Netherlands B.V. | Control based on probability density function of parameter |
| CN111788883B (zh) * | 2018-02-26 | 2021-11-05 | 株式会社高迎科技 | 部件贴装状态的检查方法、印刷电路板检查装置及计算机可读记录介质 |
| US10795346B2 (en) | 2018-03-13 | 2020-10-06 | Applied Materials, Inc. | Machine learning systems for monitoring of semiconductor processing |
| US10789703B2 (en) * | 2018-03-19 | 2020-09-29 | Kla-Tencor Corporation | Semi-supervised anomaly detection in scanning electron microscope images |
| KR20200123858A (ko) | 2018-03-21 | 2020-10-30 | 케이엘에이 코포레이션 | 합성 이미지를 사용한 머신 러닝 모델 트레이닝 |
| KR102565278B1 (ko) | 2018-03-26 | 2023-08-09 | 삼성전자주식회사 | 영상 분할 방법, 영상 분할 장치, 및 영상 분할을 위한 학습 방법 |
| US10670536B2 (en) | 2018-03-28 | 2020-06-02 | Kla-Tencor Corp. | Mode selection for inspection |
| US10599951B2 (en) * | 2018-03-28 | 2020-03-24 | Kla-Tencor Corp. | Training a neural network for defect detection in low resolution images |
| US10572697B2 (en) | 2018-04-06 | 2020-02-25 | Lam Research Corporation | Method of etch model calibration using optical scatterometry |
| US11921433B2 (en) | 2018-04-10 | 2024-03-05 | Lam Research Corporation | Optical metrology in machine learning to characterize features |
| KR102812035B1 (ko) | 2018-04-10 | 2025-05-22 | 램 리써치 코포레이션 | 레지스트 및 에칭 모델링 |
| US11460753B2 (en) | 2018-05-10 | 2022-10-04 | The Board Of Trustees Of The Leland Stanford Junior University | Systems and methods for activation functions for photonic neural networks |
| EP3791332A4 (en) * | 2018-05-10 | 2022-03-09 | The Board of Trustees of the Leland Stanford Junior University | TRAINING PHOTONIC NEURAL NETWORKS BY IN SITU BACK PROPAGATION |
| US10824909B2 (en) | 2018-05-15 | 2020-11-03 | Toyota Research Institute, Inc. | Systems and methods for conditional image translation |
| US12020167B2 (en) | 2018-05-17 | 2024-06-25 | Magic Leap, Inc. | Gradient adversarial training of neural networks |
| CN110555800A (zh) * | 2018-05-30 | 2019-12-10 | 北京三星通信技术研究有限公司 | 图像处理装置及方法 |
| US10592635B2 (en) | 2018-05-31 | 2020-03-17 | International Business Machines Corporation | Generating synthetic layout patterns by feedforward neural network based variational autoencoders |
| CN110555340B (zh) * | 2018-05-31 | 2022-10-18 | 赛灵思电子科技(北京)有限公司 | 神经网络计算方法和系统及相应的双神经网络实现 |
| US10713569B2 (en) | 2018-05-31 | 2020-07-14 | Toyota Research Institute, Inc. | System and method for generating improved synthetic images |
| US10621301B2 (en) | 2018-06-06 | 2020-04-14 | International Business Machines Corporation | Coordinates-based variational autoencoder for generating synthetic via layout patterns |
| WO2019233738A1 (en) * | 2018-06-08 | 2019-12-12 | Asml Netherlands B.V. | Metrology apparatus and method for determining a characteristic of one or more structures on a substrate |
| EP3579052A1 (en) * | 2018-06-08 | 2019-12-11 | ASML Netherlands B.V. | Metrology apparatus and method for determining a characteristic of one or more structures on a substrate |
| US11315231B2 (en) | 2018-06-08 | 2022-04-26 | Industrial Technology Research Institute | Industrial image inspection method and system and computer readable recording medium |
| US10810460B2 (en) * | 2018-06-13 | 2020-10-20 | Cosmo Artificial Intelligence—AI Limited | Systems and methods for training generative adversarial networks and use of trained generative adversarial networks |
| CN114997408A (zh) * | 2018-06-14 | 2022-09-02 | 诺威有限公司 | 半导体度量方法和半导体度量系统 |
| CN112384860B (zh) | 2018-06-15 | 2023-12-08 | Asml荷兰有限公司 | 基于机器学习的逆光学邻近效应校正和过程模型校准 |
| CN109061131A (zh) * | 2018-06-29 | 2018-12-21 | 志诺维思(北京)基因科技有限公司 | 染色图片处理方法及装置 |
| TWI689875B (zh) * | 2018-06-29 | 2020-04-01 | 由田新技股份有限公司 | 利用深度學習系統的自動光學檢測分類設備及其訓練設備 |
| US10169852B1 (en) * | 2018-07-03 | 2019-01-01 | Nanotronics Imaging, Inc. | Systems, devices, and methods for providing feedback on and improving the accuracy of super-resolution imaging |
| KR101936029B1 (ko) * | 2018-07-18 | 2019-01-11 | 한국과학기술정보연구원 | 딥러닝 기반의 가치 평가 방법 및 그 장치 |
| US10846845B2 (en) * | 2018-07-25 | 2020-11-24 | Fei Company | Training an artificial neural network using simulated specimen images |
| JP2020024121A (ja) * | 2018-08-06 | 2020-02-13 | 澁谷工業株式会社 | 物品検査装置 |
| JPWO2020031984A1 (ja) * | 2018-08-08 | 2021-08-10 | Blue Tag株式会社 | 部品の検査方法及び検査システム |
| US11386304B2 (en) | 2018-08-20 | 2022-07-12 | Samsung Electronics Co., Ltd. | Electronic device and method of controlling the same |
| WO2020055555A1 (en) * | 2018-09-12 | 2020-03-19 | Applied Materials, Inc. | Deep auto-encoder for equipment health monitoring and fault detection in semiconductor and display process equipment tools |
| US10949964B2 (en) * | 2018-09-21 | 2021-03-16 | Kla Corporation | Super-resolution defect review image generation through generative adversarial networks |
| US10314477B1 (en) | 2018-10-31 | 2019-06-11 | Capital One Services, Llc | Systems and methods for dynamically modifying visual content to account for user visual impairment |
| US12299582B2 (en) * | 2018-11-06 | 2025-05-13 | Emory University | Systems and methods for training an autoencoder neural network using sparse data |
| KR102734292B1 (ko) | 2018-11-12 | 2024-11-26 | 삼성전자주식회사 | 데이터 분류 방법 및 장치, 분류기의 학습 방법 및 장치 |
| KR102638267B1 (ko) | 2018-12-03 | 2024-02-21 | 삼성전자주식회사 | 반도체 웨이퍼 불량 분석 시스템 및 그것의 동작 방법 |
| US10740901B2 (en) * | 2018-12-17 | 2020-08-11 | Nvidia Corporation | Encoder regularization of a segmentation model |
| US11010885B2 (en) * | 2018-12-18 | 2021-05-18 | Kla Corporation | Optical-mode selection for multi-mode semiconductor inspection |
| US10923318B2 (en) * | 2018-12-20 | 2021-02-16 | Fei Company | Optical alignment correction using convolutional neural network evaluation of a beam image |
| IL284031B2 (en) * | 2018-12-31 | 2025-04-01 | Asml Netherlands Bv | Fully automated SEM sampling system for E-beam image enhancement |
| US11170475B2 (en) * | 2019-01-10 | 2021-11-09 | Kla Corporation | Image noise reduction using stacked denoising auto-encoder |
| US11347788B2 (en) | 2019-01-16 | 2022-05-31 | Toyota Research Institute, Inc. | Systems and methods for generating a requested image view |
| US11379967B2 (en) * | 2019-01-18 | 2022-07-05 | Kla Corporation | Methods and systems for inspection of semiconductor structures with automatically generated defect features |
| CN109901835B (zh) * | 2019-01-25 | 2020-09-04 | 北京三快在线科技有限公司 | 布局元素的方法、装置、设备及存储介质 |
| US10977405B2 (en) | 2019-01-29 | 2021-04-13 | Lam Research Corporation | Fill process optimization using feature scale modeling |
| US10482584B1 (en) * | 2019-01-31 | 2019-11-19 | StradVision, Inc. | Learning method and learning device for removing jittering on video acquired through shaking camera by using a plurality of neural networks for fault tolerance and fluctuation robustness in extreme situations, and testing method and testing device using the same |
| US11294162B2 (en) * | 2019-02-07 | 2022-04-05 | Nanotronics Imaging, Inc. | Fluorescence microscopy inspection systems, apparatus and methods with darkfield channel |
| US10922808B2 (en) | 2019-02-14 | 2021-02-16 | KLA—Tencor Corp. | File selection for test image to design alignment |
| US11227102B2 (en) * | 2019-03-12 | 2022-01-18 | Wipro Limited | System and method for annotation of tokens for natural language processing |
| US11047807B2 (en) * | 2019-03-25 | 2021-06-29 | Camtek Ltd. | Defect detection |
| US11551348B2 (en) * | 2019-04-09 | 2023-01-10 | KLA Corp. | Learnable defect detection for semiconductor applications |
| JP7203678B2 (ja) * | 2019-04-19 | 2023-01-13 | 株式会社日立ハイテク | 欠陥観察装置 |
| US11900026B1 (en) | 2019-04-24 | 2024-02-13 | X Development Llc | Learned fabrication constraints for optimizing physical devices |
| US11684253B2 (en) * | 2019-04-24 | 2023-06-27 | Topcon Corporation | 2D multi-layer thickness measurement with reconstructed spectrum |
| US11379633B2 (en) | 2019-06-05 | 2022-07-05 | X Development Llc | Cascading models for optimization of fabrication and design of a physical device |
| CN110246145B (zh) * | 2019-06-21 | 2023-02-21 | 福州大学 | 一种腹部ct图像的分割方法 |
| US11461519B2 (en) * | 2019-06-24 | 2022-10-04 | Nanyang Technological University | Machine learning techniques for estimating mechanical properties of materials |
| US12198298B2 (en) * | 2019-07-03 | 2025-01-14 | Korea Advanced Institute Of Science And Technology | Video processing method and apparatus |
| US11880193B2 (en) * | 2019-07-26 | 2024-01-23 | Kla Corporation | System and method for rendering SEM images and predicting defect imaging conditions of substrates using 3D design |
| US11693386B2 (en) * | 2019-08-27 | 2023-07-04 | Samsung Eleotronics Co., Ltd. | Method and electronic device for guiding semiconductor manufacturing process |
| US11727169B2 (en) | 2019-09-11 | 2023-08-15 | Toyota Research Institute, Inc. | Systems and methods for inferring simulated data |
| US11126891B2 (en) | 2019-09-11 | 2021-09-21 | Toyota Research Institute, Inc. | Systems and methods for simulating sensor data using a generative model |
| CN114402342A (zh) * | 2019-09-16 | 2022-04-26 | Asml荷兰有限公司 | 用于生成特性图案以及训练机器学习模型的方法 |
| US11494695B2 (en) | 2019-09-27 | 2022-11-08 | Google Llc | Training neural networks to generate structured embeddings |
| US11580650B2 (en) | 2019-10-01 | 2023-02-14 | KLA Corp. | Multi-imaging mode image alignment |
| AU2020358062A1 (en) * | 2019-10-01 | 2022-04-21 | Chevron U.S.A. Inc. | Artificial learning fracture system and method for predicting permeability of hydrocarbon reservoirs |
| US11087449B2 (en) | 2019-10-24 | 2021-08-10 | KLA Corp. | Deep learning networks for nuisance filtering |
| KR102144975B1 (ko) * | 2019-11-08 | 2020-08-14 | 주식회사 알체라 | 머신 러닝 시스템 및 머신 러닝 시스템의 동작 방법 |
| CN110929864B (zh) * | 2019-12-05 | 2023-04-18 | 北京超放信息技术有限公司 | 光学衍射神经网络在线训练方法及系统 |
| KR102282989B1 (ko) * | 2019-12-26 | 2021-07-29 | 주식회사 나눔에너지 | 머신러닝을 이용한 태양광패널 설치용 지붕 가장자리 이미지 추출 시스템 |
| SE1930421A1 (en) * | 2019-12-30 | 2021-07-01 | Unibap Ab | Method and means for detection of imperfections in products |
| US12124944B2 (en) | 2020-01-03 | 2024-10-22 | Silicon Storage Technology, Inc. | Precise data tuning method and apparatus for analog neural memory in an artificial neural network |
| US11636322B2 (en) | 2020-01-03 | 2023-04-25 | Silicon Storage Technology, Inc. | Precise data tuning method and apparatus for analog neural memory in an artificial neural network |
| EP3885813B1 (en) * | 2020-03-27 | 2025-12-10 | Leica Microsystems CMS GmbH | Method and device for estimating a sted resolution |
| US12131103B2 (en) | 2020-03-30 | 2024-10-29 | Kla Corporation | Semiconductor fabrication process parameter determination using a generative adversarial network |
| CN111461300B (zh) * | 2020-03-30 | 2022-10-14 | 北京航空航天大学 | 光学残差深度网络构建方法 |
| CN111524078B (zh) * | 2020-04-20 | 2023-04-18 | 浙江大学 | 一种基于稠密网络的显微镜图像去模糊方法 |
| US11796794B2 (en) | 2020-05-12 | 2023-10-24 | The Board Of Trustees Of The Leland Stanford Junior University | Multi-objective, robust constraints enforced global topology optimizer for optical devices |
| US11531842B2 (en) | 2020-05-20 | 2022-12-20 | Toyota Research Institute, Inc. | Invertible depth network for image reconstruction and domain transfers |
| US11769242B2 (en) | 2020-05-21 | 2023-09-26 | Kla Corporation | Mode selection and defect detection training |
| CN113761979B (zh) * | 2020-06-04 | 2023-11-14 | 富士通株式会社 | 用于优化模型的方法、设备和存储介质 |
| DE102021205690A1 (de) * | 2020-06-05 | 2021-12-09 | Nvidia Corporation | Trainieren neuronaler Netze mit begrenzten Daten unter Verwendung invertierbarer Augmentationsoperatoren |
| US12321825B2 (en) | 2020-06-05 | 2025-06-03 | Nvidia Corporation | Training neural networks with limited data using invertible augmentation operators |
| US12061862B2 (en) | 2020-06-11 | 2024-08-13 | Capital One Services, Llc | Systems and methods for generating customized content based on user preferences |
| US20230169352A1 (en) * | 2020-06-12 | 2023-06-01 | Haijun Zhao | Generate source code to build secure machine learning engine for edge devices and existing toolchains |
| US11232554B1 (en) * | 2021-06-07 | 2022-01-25 | Elementary Robotics, Inc. | Machine-learning based camera image triggering for quality assurance inspection processes |
| WO2022002399A1 (en) * | 2020-07-02 | 2022-01-06 | Ecole Polytechnique Federale De Lausanne (Epfl) | Multicore fiber endoscope for phase imaging based on intensity recording using deep neural networks |
| US20220075916A1 (en) | 2020-09-07 | 2022-03-10 | Kla Corporation | System and method for accelerating physical simulation models during microelectronic device fabrication |
| CN112200218B (zh) * | 2020-09-10 | 2023-06-20 | 浙江大华技术股份有限公司 | 一种模型训练方法、装置及电子设备 |
| US11530913B2 (en) * | 2020-09-24 | 2022-12-20 | Kla Corporation | Methods and systems for determining quality of semiconductor measurements |
| CN114330471A (zh) * | 2020-09-25 | 2022-04-12 | 辉达公司 | 基于能量的变分自动编码器 |
| US12372864B2 (en) | 2020-10-22 | 2025-07-29 | D2S, Inc. | Methods and systems to determine shapes for semiconductor or flat panel display fabrication |
| EP4264518A4 (en) * | 2020-12-18 | 2024-12-11 | Strong Force VCN Portfolio 2019, LLC | ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR VALUE CHAIN NETWORKS |
| CN112301322B (zh) * | 2020-12-21 | 2021-04-13 | 上海陛通半导体能源科技股份有限公司 | 具有工艺参数智能调节功能的气相沉积设备及方法 |
| US20220270212A1 (en) * | 2021-02-25 | 2022-08-25 | Kla Corporation | Methods for improving optical inspection and metrology image quality using chip design data |
| EP4610886A3 (en) * | 2021-02-25 | 2025-09-24 | Silicon Storage Technology, Inc. | Precise data tuning method and apparatus for analog neural memory in an artificial neural network |
| CN113032778B (zh) * | 2021-03-02 | 2021-09-21 | 四川大学 | 一种基于行为特征编码的半监督网络异常行为检测方法 |
| EP4301548A4 (en) * | 2021-03-03 | 2025-01-29 | Applied Materials, Inc. | IN-SITU MONITORING FOR MARKING TRAINING SPECTRA FOR A MACHINE LEARNING SYSTEM FOR SPECTROGRAPHIC MONITORING |
| CN113239978B (zh) * | 2021-04-22 | 2024-06-04 | 科大讯飞股份有限公司 | 医学图像预处理模型与分析模型的相关方法和装置 |
| US12469725B2 (en) * | 2021-06-27 | 2025-11-11 | Delta Design, Inc. | Method for determining corrective film pattern to reduce semiconductor wafer bow |
| WO2023008983A1 (ko) | 2021-07-30 | 2023-02-02 | 주식회사 딥엑스 | 이미지 신호 프로세서의 제어 방법 및 이를 수행하는 제어 장치 |
| KR20240051271A (ko) * | 2021-09-03 | 2024-04-19 | 램 리써치 코포레이션 | 웨이퍼 프로세싱 툴의 머신 비전 검사 (machine vision inspection) |
| US11868689B2 (en) * | 2021-10-11 | 2024-01-09 | KLA Corp. | Systems and methods for setting up a physics-based model |
| CN114036607B (zh) * | 2021-11-03 | 2022-07-01 | 清华大学 | 多模态输入深度神经网络、框架结构梁柱设计方法及装置 |
| CN114170107B (zh) * | 2021-12-13 | 2024-06-11 | 浙江理工大学 | 一种基于生成对抗网络的浑浊水下偏振图像复原方法 |
| US20230229844A1 (en) | 2022-01-19 | 2023-07-20 | D2S, Inc. | Interactively presenting for minimum overlap shapes in an ic design |
| CN114564768B (zh) * | 2022-03-03 | 2024-12-24 | 上海大学 | 一种基于深度学习的端到端智能平面设计方法 |
| US20230314336A1 (en) | 2022-03-31 | 2023-10-05 | Kla Corporation | Multi-mode optical inspection |
| US11922619B2 (en) | 2022-03-31 | 2024-03-05 | Kla Corporation | Context-based defect inspection |
| CN114880739B (zh) * | 2022-04-25 | 2023-03-24 | 清华大学 | 生成式建筑结构设计方案的再优化设计方法和装置 |
| US12223641B2 (en) * | 2022-05-19 | 2025-02-11 | Applied Materials Israel Ltd. | Defect detection of a semiconductor specimen |
| CN114881990B (zh) * | 2022-05-23 | 2025-10-31 | 北京御微半导体技术有限公司 | 掩模版缺陷检测方法、装置、电子设备及存储介质 |
| US20230418995A1 (en) * | 2022-06-23 | 2023-12-28 | Onto Innovation Inc. | Multiple sources of signals for hybrid metrology using physical modeling and machine learning |
| CN115935779B (zh) * | 2022-08-01 | 2023-09-08 | 先进半导体材料(安徽)有限公司 | 刻蚀仿真模型的构建方法 |
| JP2024021487A (ja) * | 2022-08-03 | 2024-02-16 | JDI Design and Development 合同会社 | 検査方法、検査装置及びプログラム |
| US12489020B2 (en) * | 2022-09-19 | 2025-12-02 | Applied Materials Israel Ltd. | End-to-end measurement for semiconductor specimens |
| WO2024088665A1 (en) * | 2022-10-23 | 2024-05-02 | Asml Netherlands B.V. | Training a machine learning model to predict images representative of defects on a substrate |
| TWI870179B (zh) * | 2023-12-25 | 2025-01-11 | 財團法人工業技術研究院 | 神經網路模型的深度學習編譯方法及儲存對應程式之非暫態電腦可讀取媒體 |
| US20250225638A1 (en) * | 2024-01-09 | 2025-07-10 | Kla Corporation | Digital nonlinear neural network based image filtering for semiconductor applications |
| CN119295681B (zh) * | 2024-12-13 | 2025-04-04 | 四川轻化工大学 | 基于ga-bp神经网络的辐射场三维重建方法 |
Family Cites Families (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH08136466A (ja) * | 1994-11-10 | 1996-05-31 | Dainippon Screen Mfg Co Ltd | 画像パターン検査装置 |
| US5711843A (en) * | 1995-02-21 | 1998-01-27 | Orincon Technologies, Inc. | System for indirectly monitoring and controlling a process with particular application to plasma processes |
| US6466314B1 (en) | 1998-09-17 | 2002-10-15 | Applied Materials, Inc. | Reticle design inspection system |
| US6641746B2 (en) * | 2001-09-28 | 2003-11-04 | Agere Systems, Inc. | Control of semiconductor processing |
| JP2003243470A (ja) * | 2002-02-18 | 2003-08-29 | Mitsubishi Electric Corp | 異常検出システム、プログラムおよび記録媒体 |
| US7346208B2 (en) | 2003-10-25 | 2008-03-18 | Hewlett-Packard Development Company, L.P. | Image artifact reduction using a neural network |
| US7729529B2 (en) * | 2004-12-07 | 2010-06-01 | Kla-Tencor Technologies Corp. | Computer-implemented methods for detecting and/or sorting defects in a design pattern of a reticle |
| US7676077B2 (en) | 2005-11-18 | 2010-03-09 | Kla-Tencor Technologies Corp. | Methods and systems for utilizing design data in combination with inspection data |
| US7570796B2 (en) | 2005-11-18 | 2009-08-04 | Kla-Tencor Technologies Corp. | Methods and systems for utilizing design data in combination with inspection data |
| US8698093B1 (en) | 2007-01-19 | 2014-04-15 | Kla-Tencor Corporation | Objective lens with deflector plates immersed in electrostatic lens field |
| WO2008133951A2 (en) * | 2007-04-24 | 2008-11-06 | Massachusetts Institute Of Technology | Method and apparatus for image processing |
| CN101785009B (zh) * | 2007-08-20 | 2012-10-10 | 恪纳腾公司 | 确定实际缺陷是潜在系统性缺陷还是潜在随机缺陷的计算机实现的方法 |
| US8126255B2 (en) | 2007-09-20 | 2012-02-28 | Kla-Tencor Corp. | Systems and methods for creating persistent data for a wafer and for using persistent data for inspection-related functions |
| US8238635B2 (en) | 2008-03-21 | 2012-08-07 | General Electric Company | Method and system for identifying defects in radiographic image data corresponding to a scanned object |
| CN102077230A (zh) | 2008-04-17 | 2011-05-25 | 旅行者保险公司 | 用于确定和处理物体结构状况信息的方法和系统 |
| US8577820B2 (en) * | 2011-03-04 | 2013-11-05 | Tokyo Electron Limited | Accurate and fast neural network training for library-based critical dimension (CD) metrology |
| US8664594B1 (en) | 2011-04-18 | 2014-03-04 | Kla-Tencor Corporation | Electron-optical system for high-speed and high-sensitivity inspections |
| US8692204B2 (en) | 2011-04-26 | 2014-04-08 | Kla-Tencor Corporation | Apparatus and methods for electron beam detection |
| US8761476B2 (en) * | 2011-11-09 | 2014-06-24 | The Johns Hopkins University | Hyperspectral imaging for detection of skin related conditions |
| US8716662B1 (en) | 2012-07-16 | 2014-05-06 | Kla-Tencor Corporation | Methods and apparatus to review defects using scanning electron microscope with multiple electron beam configurations |
| US9189844B2 (en) * | 2012-10-15 | 2015-11-17 | Kla-Tencor Corp. | Detecting defects on a wafer using defect-specific information |
| US9222895B2 (en) | 2013-02-25 | 2015-12-29 | Kla-Tencor Corp. | Generalized virtual inspector |
| US9430824B2 (en) * | 2013-05-14 | 2016-08-30 | Kla-Tencor Corporation | Machine learning method and apparatus for inspecting reticles |
| US9183624B2 (en) * | 2013-06-19 | 2015-11-10 | Kla-Tencor Corp. | Detecting defects on a wafer with run time use of design data |
| US9612541B2 (en) * | 2013-08-20 | 2017-04-04 | Kla-Tencor Corporation | Qualifying patterns for microlithography |
| 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 |
| US10650508B2 (en) * | 2014-12-03 | 2020-05-12 | Kla-Tencor Corporation | Automatic defect classification without sampling and feature selection |
| US10062543B2 (en) | 2015-06-23 | 2018-08-28 | Kla-Tencor Corp. | Determining multi-patterning step overlay error |
| US10416087B2 (en) | 2016-01-01 | 2019-09-17 | Kla-Tencor Corporation | Systems and methods for defect detection using image reconstruction |
| US10319076B2 (en) * | 2016-06-16 | 2019-06-11 | Facebook, Inc. | Producing higher-quality samples of natural images |
| US10043088B2 (en) * | 2016-06-23 | 2018-08-07 | Siemens Healthcare Gmbh | Image quality score using a deep generative machine-learning model |
-
2017
- 2017-05-31 US US15/609,009 patent/US10346740B2/en active Active
- 2017-06-01 WO PCT/US2017/035494 patent/WO2017210455A1/en not_active Ceased
- 2017-06-01 JP JP2018563511A patent/JP6853273B2/ja active Active
- 2017-06-01 EP EP17807494.4A patent/EP3465552B1/en active Active
- 2017-06-01 CN CN201780033819.XA patent/CN109313724B/zh active Active
- 2017-06-01 TW TW106118058A patent/TWI715773B/zh active
- 2017-06-01 KR KR1020187037824A patent/KR102213730B1/ko active Active
-
2018
- 2018-11-05 IL IL262787A patent/IL262787B/en active IP Right Grant
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220121956A1 (en) * | 2020-10-16 | 2022-04-21 | Samsung Electronics Co., Ltd. | Method of training deep learning model for predicting pattern characteristics and method of manufacturing semiconductor device |
| US12205040B2 (en) * | 2020-10-16 | 2025-01-21 | Samsung Electronics Co., Ltd. | Method of training deep learning model for predicting pattern characteristics and method of manufacturing semiconductor device |
Also Published As
| Publication number | Publication date |
|---|---|
| IL262787A (en) | 2018-12-31 |
| KR20190004000A (ko) | 2019-01-10 |
| TWI715773B (zh) | 2021-01-11 |
| EP3465552A1 (en) | 2019-04-10 |
| IL262787B (en) | 2020-09-30 |
| EP3465552A4 (en) | 2020-01-22 |
| US20170351952A1 (en) | 2017-12-07 |
| KR102213730B1 (ko) | 2021-02-05 |
| EP3465552B1 (en) | 2023-05-24 |
| CN109313724A (zh) | 2019-02-05 |
| TW201802726A (zh) | 2018-01-16 |
| US10346740B2 (en) | 2019-07-09 |
| WO2017210455A1 (en) | 2017-12-07 |
| JP2019525450A (ja) | 2019-09-05 |
| CN109313724B (zh) | 2021-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6853273B2 (ja) | ニューラルネットワークと順物理モデルを半導体用途に組み込んだシステムおよび方法 | |
| KR102321953B1 (ko) | 다양한 모댈리티들로 획득된 이미지들의 정렬을 위한 학습 기반 접근 방식 | |
| JP6758418B2 (ja) | 半導体用途のための、入力画像からのシミュレーション画像の生成 | |
| KR102637409B1 (ko) | 반도체 적용들을 위한 저해상도 이미지들로부터 고해상도 이미지들의 생성 | |
| JP6893514B2 (ja) | ハイブリッドインスペクタ | |
| TWI713672B (zh) | 為樣品產生模擬輸出之系統,非暫時性電腦可讀媒體及電腦實施方法 | |
| KR20190072569A (ko) | 반도체 애플리케이션을 위해 구성된 심층 학습 모델을 위한 진단 시스템 및 방법 | |
| CN108475351A (zh) | 用于半导体应用的基于机器学习的模型的加速训练 | |
| CN110785709B (zh) | 从低分辨率图像产生高分辨率图像以用于半导体应用 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20200601 |
|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20200601 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20200618 |
|
| A871 | Explanation of circumstances concerning accelerated examination |
Free format text: JAPANESE INTERMEDIATE CODE: A871 Effective date: 20200618 |
|
| A975 | Report on accelerated examination |
Free format text: JAPANESE INTERMEDIATE CODE: A971005 Effective date: 20200828 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20200908 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20201204 |
|
| 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: 20210302 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20210311 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 6853273 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |
|
| R250 | Receipt of annual fees |
Free format text: JAPANESE INTERMEDIATE CODE: R250 |
|
| R250 | Receipt of annual fees |
Free format text: JAPANESE INTERMEDIATE CODE: R250 |