AT524013A5 - Verfahren und Systeme zur Klassifizierung von Merkmalen in elektronischen Entwürfen - Google Patents

Verfahren und Systeme zur Klassifizierung von Merkmalen in elektronischen Entwürfen Download PDF

Info

Publication number
AT524013A5
AT524013A5 ATA9073/2020A AT90732020A AT524013A5 AT 524013 A5 AT524013 A5 AT 524013A5 AT 90732020 A AT90732020 A AT 90732020A AT 524013 A5 AT524013 A5 AT 524013A5
Authority
AT
Austria
Prior art keywords
methods
features
systems
electronic designs
classifying features
Prior art date
Application number
ATA9073/2020A
Other languages
English (en)
Other versions
AT524013A2 (de
Inventor
Niewczas Mariusz
Shendre Abhishek
Original Assignee
D2S Inc
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 D2S Inc filed Critical D2S Inc
Publication of AT524013A2 publication Critical patent/AT524013A2/de
Publication of AT524013A5 publication Critical patent/AT524013A5/de

Links

Classifications

    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
    • G03F1/68—Preparation processes not covered by groups G03F1/20 - G03F1/50
    • G03F1/70—Adapting basic layout or design of masks to lithographic process requirements, e.g., second iteration correction of mask patterns for imaging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
    • G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
    • G03F1/36—Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes
    • 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
    • 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
    • G06F18/24137—Distances to cluster centroïds
    • 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
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive 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
    • G06T7/0006—Industrial image inspection using a design-rule based approach
    • 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
    • G06T7/001—Industrial image inspection using an image reference approach
    • 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
    • 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
    • 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
    • G06T2207/30121—CRT, LCD or plasma display
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Multimedia (AREA)
  • Databases & Information Systems (AREA)
  • Quality & Reliability (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)
  • Preparing Plates And Mask In Photomechanical Process (AREA)

Abstract

Verfahren zum Abgleichen von Merkmalen in Mustern für elektronische Entwürfe beinhalten die Eingabe eines Satzes von Musterdaten für Halbleiter- oder Flachbildschirmanzeigen, wobei der Satz von Musterdaten eine Vielzahl von Merkmalen umfasst. Jedes Merkmal aus der Vielzahl der Merkmale wird klassifiziert, wobei das Klassifizieren auf einem geometrischen Kontext basiert, der durch Formen in einer Region definiert ist. Beim Klassifizieren werden Techniken des maschinellen Lernens verwendet.
ATA9073/2020A 2019-02-25 2020-02-22 Verfahren und Systeme zur Klassifizierung von Merkmalen in elektronischen Entwürfen AT524013A5 (de)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201962810168P 2019-02-25 2019-02-25
US16/793,390 US11263496B2 (en) 2019-02-25 2020-02-18 Methods and systems to classify features in electronic designs
PCT/IB2020/051496 WO2020174342A1 (en) 2019-02-25 2020-02-22 Methods and systems to classify features in electronic designs

Publications (2)

Publication Number Publication Date
AT524013A2 AT524013A2 (de) 2022-01-15
AT524013A5 true AT524013A5 (de) 2024-11-15

Family

ID=72141725

Family Applications (1)

Application Number Title Priority Date Filing Date
ATA9073/2020A AT524013A5 (de) 2019-02-25 2020-02-22 Verfahren und Systeme zur Klassifizierung von Merkmalen in elektronischen Entwürfen

Country Status (6)

Country Link
US (1) US11263496B2 (de)
JP (1) JP7550158B2 (de)
KR (1) KR102886571B1 (de)
CN (1) CN113474727B (de)
AT (1) AT524013A5 (de)
WO (1) WO2020174342A1 (de)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11263496B2 (en) 2019-02-25 2022-03-01 D2S, Inc. Methods and systems to classify features in electronic designs
US11182929B2 (en) * 2019-02-25 2021-11-23 Center For Deep Learning In Electronics Manufacturing, Inc. Methods and systems for compressing shape data for electronic designs
US20210181253A1 (en) * 2019-12-16 2021-06-17 Texas Instruments Incorporated Fail Density-Based Clustering for Yield Loss Detection
US12400314B2 (en) * 2021-09-13 2025-08-26 Applied Materials Israel Ltd. Mask inspection for semiconductor specimen fabrication
CN116266407A (zh) * 2021-12-17 2023-06-20 三星电子株式会社 使用深度神经网络的基于图像的半导体器件图案化方法
US20240161264A1 (en) * 2022-11-15 2024-05-16 Micron Technology, Inc. Defect characterization in semiconductor devices based on image processing
IL299017B2 (en) * 2022-12-12 2024-09-01 Applied Materials Israel Ltd Automatic segmentation of an image of a semiconductor specimen and usage in metrology

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140341462A1 (en) * 2013-05-14 2014-11-20 Kla-Tencor Corporation Machine learning method and apparatus for inspecting reticles
US20140358830A1 (en) * 2013-05-30 2014-12-04 Synopsys, Inc. Lithographic hotspot detection using multiple machine learning kernels
US20180293721A1 (en) * 2017-04-07 2018-10-11 Kla-Tencor Corporation Contour based defect detection
EP3392712A1 (de) * 2017-04-21 2018-10-24 IMEC vzw Verfahren zur analyse des entwurfs eines integrierten schaltkreises
US20180322234A1 (en) * 2017-05-08 2018-11-08 Globalfoundries Inc. Prediction of process-sensitive geometries with machine learning

Family Cites Families (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001126980A (ja) 1999-11-01 2001-05-11 Nikon Corp 蓄積エネルギー計算方法、プログラム記憶媒体、近接効果計算方法、マスク又はレチクルパターンの設計方法、及び半導体デバイスの製造方法
JP4710191B2 (ja) * 2001-08-01 2011-06-29 凸版印刷株式会社 フォトマスクパターン形状自動計測方法及びその装置及びそのプログラム及びフォトマスク製造方法及び半導体装置の製造方法
US6892367B2 (en) * 2002-06-28 2005-05-10 Pdf Solutions, Inc. Vertex based layout pattern (VEP): a method and apparatus for describing repetitive patterns in IC mask layout
JP2004296592A (ja) * 2003-03-26 2004-10-21 Dainippon Screen Mfg Co Ltd 欠陥分類装置、欠陥分類方法およびプログラム
JP2004354251A (ja) * 2003-05-29 2004-12-16 Nidek Co Ltd 欠陥検査装置
JP2006220644A (ja) * 2005-01-14 2006-08-24 Hitachi High-Technologies Corp パターン検査方法及びその装置
JP2008076505A (ja) 2006-09-19 2008-04-03 Nec Electronics Corp マスク設計方法およびこれを用いた半導体装置の製造方法、ならびにマスク設計システム
US8103086B2 (en) * 2007-01-11 2012-01-24 Kla-Tencor Corporation Reticle defect inspection with model-based thin line approaches
US8490034B1 (en) * 2010-07-08 2013-07-16 Gauda, Inc. Techniques of optical proximity correction using GPU
US8315453B2 (en) * 2010-07-27 2012-11-20 Applied Materials Israel, Ltd. Defect classification with optimized purity
US10330608B2 (en) * 2012-05-11 2019-06-25 Kla-Tencor Corporation Systems and methods for wafer surface feature detection, classification and quantification with wafer geometry metrology tools
US9057965B2 (en) 2012-12-03 2015-06-16 Taiwan Semiconductor Manufacturing Company, Ltd. Method of generating a set of defect candidates for wafer
US9354511B2 (en) 2013-12-06 2016-05-31 Synopsys, Inc. Integrated mask-aware lithography modeling to support off-axis illumination and multi-tone masks
TWI510482B (zh) 2014-03-07 2015-12-01 Ind Tech Res Inst 聯苯衍生物及有機電致發光裝置
US9972491B2 (en) 2014-06-27 2018-05-15 Toshiba Memory Corporation Mask data generation method, mask data generation system, and recording medium
US10025175B2 (en) 2014-09-12 2018-07-17 Taiwan Semiconductor Manufacturing Company, Ltd. Method and system to prepare, manufacture and inspect mask patterns for a semiconductor device
US10074036B2 (en) * 2014-10-21 2018-09-11 Kla-Tencor Corporation Critical dimension uniformity enhancement techniques and apparatus
US10650508B2 (en) * 2014-12-03 2020-05-12 Kla-Tencor Corporation Automatic defect classification without sampling and feature selection
US20170357911A1 (en) * 2014-12-18 2017-12-14 Asml Netherlands B.V. Feature search by machine learning
JP6198805B2 (ja) 2015-02-16 2017-09-20 キヤノン株式会社 リソグラフィ装置、リソグラフィ方法、プログラム、リソグラフィシステムおよび物品製造方法
US9500945B1 (en) * 2015-06-09 2016-11-22 Globalfoundries Singapore Pte. Ltd. Pattern classification based proximity corrections for reticle fabrication
US10140698B2 (en) * 2015-08-10 2018-11-27 Kla-Tencor Corporation Polygon-based geometry classification for semiconductor mask inspection
US9965901B2 (en) 2015-11-19 2018-05-08 KLA—Tencor Corp. Generating simulated images from design information
US10395356B2 (en) * 2016-05-25 2019-08-27 Kla-Tencor Corp. Generating simulated images from input images for semiconductor applications
US20210294218A1 (en) 2016-08-19 2021-09-23 Asml Netherlands B.V. Modeling post-exposure processes
US9990460B2 (en) 2016-09-30 2018-06-05 Taiwan Semiconductor Manufacturing Co., Ltd. Source beam optimization method for improving lithography printability
US10527928B2 (en) 2016-12-20 2020-01-07 Taiwan Semiconductor Manufacturing Co., Ltd. Optical proximity correction methodology using pattern classification for target placement
US10248663B1 (en) 2017-03-03 2019-04-02 Descartes Labs, Inc. Geo-visual search
US10198550B2 (en) 2017-04-04 2019-02-05 Globalfoundries Inc. SRAF insertion with artificial neural network
US10733744B2 (en) * 2017-05-11 2020-08-04 Kla-Tencor Corp. Learning based approach for aligning images acquired with different modalities
JP7146751B2 (ja) 2017-06-22 2022-10-04 株式会社半導体エネルギー研究所 レイアウト設計システム及びレイアウト設計方法
CN109409354B (zh) 2017-08-18 2021-09-21 深圳市道通智能航空技术股份有限公司 无人机智能跟随目标确定方法、无人机和遥控器
US11543830B2 (en) 2017-12-06 2023-01-03 Petuum, Inc. Unsupervised real-to-virtual domain unification for end-to-end highway driving
DE102017222616A1 (de) 2017-12-13 2019-06-13 Robert Bosch Gmbh Verfahren zum automatisierten Erstellen von Regeln für eine regelbasierte Anomalieerkennung in einem Datenstrom
KR20190073756A (ko) * 2017-12-19 2019-06-27 삼성전자주식회사 반도체 결함 분류 장치, 반도체의 결함을 분류하는 방법, 그리고 반도체 결함 분류 시스템
US10657213B2 (en) * 2017-12-22 2020-05-19 D2S, Inc. Modeling of a design in reticle enhancement technology
US11093830B2 (en) 2018-01-30 2021-08-17 D5Ai Llc Stacking multiple nodal networks
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
US10789703B2 (en) 2018-03-19 2020-09-29 Kla-Tencor Corporation Semi-supervised anomaly detection in scanning electron microscope images
US20200073636A1 (en) 2018-08-31 2020-03-05 Qualcomm Incorporated Multiply-accumulate (mac) operations for convolutional neural networks
US10884395B2 (en) 2018-12-22 2021-01-05 D2S, Inc. Method and system of reducing charged particle beam write time
US11263737B2 (en) * 2019-01-10 2022-03-01 Lam Research Corporation Defect classification and source analysis for semiconductor equipment
US11263496B2 (en) 2019-02-25 2022-03-01 D2S, Inc. Methods and systems to classify features in electronic designs

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140341462A1 (en) * 2013-05-14 2014-11-20 Kla-Tencor Corporation Machine learning method and apparatus for inspecting reticles
US20140358830A1 (en) * 2013-05-30 2014-12-04 Synopsys, Inc. Lithographic hotspot detection using multiple machine learning kernels
US20180293721A1 (en) * 2017-04-07 2018-10-11 Kla-Tencor Corporation Contour based defect detection
EP3392712A1 (de) * 2017-04-21 2018-10-24 IMEC vzw Verfahren zur analyse des entwurfs eines integrierten schaltkreises
US20180322234A1 (en) * 2017-05-08 2018-11-08 Globalfoundries Inc. Prediction of process-sensitive geometries with machine learning

Also Published As

Publication number Publication date
WO2020174342A1 (en) 2020-09-03
AT524013A2 (de) 2022-01-15
CN113474727B (zh) 2024-08-02
KR102886571B1 (ko) 2025-11-13
US20200272865A1 (en) 2020-08-27
JP2022521351A (ja) 2022-04-06
CN113474727A (zh) 2021-10-01
US11263496B2 (en) 2022-03-01
KR20210121159A (ko) 2021-10-07
JP7550158B2 (ja) 2024-09-12

Similar Documents

Publication Publication Date Title
MX2021014114A (es) Optimizacion de los datos de entrenamiento para la clasificacion de imagenes.
EP2657863A3 (de) Verfahren und Computerprogrammprodukte zur Erzeugung von Greifmustern zur Verwendung durch einen Roboter
JP2017117406A5 (de)
WO2016086024A3 (en) Learning contour identification system using portable contour metrics derived from contour mappings
EP3339221A3 (de) Vorrichtung und verfahren zum handhaben von produkten
Drolet et al. Model comparisons of the effectiveness and cost-effectiveness of vaccination: a systematic review of the literature
IN2015CH04673A (de)
Grbić et al. A method for solving the multiple ellipses detection problem
ATE545930T1 (de) Verfahren und vorrichtung zum erzeugen eines fingerabdrucks und verfahren und vorrichtung zum identifizieren eines audiosignals
AT515154A3 (de) Verfahren zum Erstellen eines Modell-Ensembles
JP2013114655A5 (de)
EP3819854A4 (de) Preisangebotsverfahren, das von einem computer ausgeführt wird, preisangebotsvorrichtung, elektronische vorrichtung und speichermedium
EP4422344C0 (de) Verfahren und vorrichtung zur ressourcenzuweisung durch begrenzung von rus und mrus für sta mit nur 20 mhz in einem wlan-system
EP4309534A3 (de) Verfahren und vorrichtung zum bereitstellen zumindest eines schnittmusters eines individuell für einen kunden anzufertigenden kleidungsstücks
Gonçalves et al. Identifying HCI approaches to support CMMI-DEV for interactive system development
Hu Flow-based tolerance rough sets for pattern classification
EP3908962C0 (de) Verfahren zum erzeugen von informationen über die herstellung einer handgeschriebenen, handbefestigten oder gedruckten spur
CN103049760A (zh) 基于图像分块和位置加权的稀疏表示目标识别方法
EP4431871A3 (de) Verfahren und vorrichtung zum erzeugen eines prüfplans für die prüfung eines messobjekts, verfahren und vorrichtung zum prüfen eines messobjekts und sowie computerprogrammprodukt
SG10201803301RA (en) Management method of processing tool
EP4202796C0 (de) Verfahren, vorrichtungen und computerprogrammprodukt zur einstellung eines klassifikators mit quantenberechnung
RU2014125265A (ru) Система и способ динамической локализации установленного в изделии дефекта
MX2025007578A (es) Metodo implementado por ordenador para seleccionar biomarcadores funcionales con el fin de identificar una afeccion objetivo en un sujeto
Dewi et al. Analysis of clustering for grouping of productive industry by k-medoid method
CN103530647B (zh) 基于分数傅里叶变换的纹理分类方法