CA3166581A1 - Method and system for training inspection equipment for automatic defect classification - Google Patents
Method and system for training inspection equipment for automatic defect classification Download PDFInfo
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- CA3166581A1 CA3166581A1 CA3166581A CA3166581A CA3166581A1 CA 3166581 A1 CA3166581 A1 CA 3166581A1 CA 3166581 A CA3166581 A CA 3166581A CA 3166581 A CA3166581 A CA 3166581A CA 3166581 A1 CA3166581 A1 CA 3166581A1
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- 230000007547 defect Effects 0.000 title claims abstract description 152
- 238000000034 method Methods 0.000 title claims abstract description 104
- 238000012549 training Methods 0.000 title claims description 203
- 238000013528 artificial neural network Methods 0.000 claims abstract description 98
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- 238000010801 machine learning Methods 0.000 claims abstract description 19
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/26—Testing of individual semiconductor devices
- G01R31/265—Contactless testing
- G01R31/2656—Contactless testing using non-ionising electromagnetic radiation, e.g. optical radiation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
- G06N5/025—Extracting rules from data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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
- G06V10/87—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using selection of the recognition techniques, e.g. of a classifier in a multiple classifier system
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2832—Specific tests of electronic circuits not provided for elsewhere
- G01R31/2834—Automated test systems [ATE]; using microprocessors or computers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2851—Testing of integrated circuits [IC]
- G01R31/2894—Aspects of quality control [QC]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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; CALCULATING OR 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
- G06T2207/30141—Printed circuit board [PCB]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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)
- Computing Systems (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Multimedia (AREA)
- Medical Informatics (AREA)
- Databases & Information Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Quality & Reliability (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Electromagnetism (AREA)
- Toxicology (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202063028800P | 2020-05-22 | 2020-05-22 | |
| US63/028,800 | 2020-05-22 | ||
| PCT/CA2021/050672 WO2021232149A1 (en) | 2020-05-22 | 2021-05-17 | Method and system for training inspection equipment for automatic defect classification |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA3166581A1 true CA3166581A1 (en) | 2021-11-25 |
Family
ID=78708867
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA3166581A Pending CA3166581A1 (en) | 2020-05-22 | 2021-05-17 | Method and system for training inspection equipment for automatic defect classification |
Country Status (5)
| Country | Link |
|---|---|
| JP (1) | JP2023528688A (zh) |
| CN (1) | CN115668286A (zh) |
| CA (1) | CA3166581A1 (zh) |
| TW (1) | TW202203152A (zh) |
| WO (1) | WO2021232149A1 (zh) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023146946A1 (en) * | 2022-01-27 | 2023-08-03 | Te Connectivity Solutions Gmbh | Vision inspection system for defect detection |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114511503B (zh) * | 2021-12-30 | 2024-05-17 | 广西慧云信息技术有限公司 | 一种自适应板材厚度的刨花板表面缺陷检测方法 |
| TWI806500B (zh) * | 2022-03-18 | 2023-06-21 | 廣達電腦股份有限公司 | 影像分類裝置和方法 |
| CN115830403B (zh) * | 2023-02-22 | 2023-05-30 | 厦门微亚智能科技有限公司 | 一种基于深度学习的自动缺陷分类系统及方法 |
| CN116245846B (zh) * | 2023-03-08 | 2023-11-21 | 华院计算技术(上海)股份有限公司 | 带钢的缺陷检测方法及装置、存储介质、计算设备 |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160132787A1 (en) * | 2014-11-11 | 2016-05-12 | Massachusetts Institute Of Technology | Distributed, multi-model, self-learning platform for machine learning |
| US10234848B2 (en) * | 2017-05-24 | 2019-03-19 | Relativity Space, Inc. | Real-time adaptive control of additive manufacturing processes using machine learning |
| US10496902B2 (en) * | 2017-09-21 | 2019-12-03 | International Business Machines Corporation | Data augmentation for image classification tasks |
| KR20190073756A (ko) * | 2017-12-19 | 2019-06-27 | 삼성전자주식회사 | 반도체 결함 분류 장치, 반도체의 결함을 분류하는 방법, 그리고 반도체 결함 분류 시스템 |
| US11429894B2 (en) * | 2018-02-28 | 2022-08-30 | Google Llc | Constrained classification and ranking via quantiles |
| US10713769B2 (en) * | 2018-06-05 | 2020-07-14 | Kla-Tencor Corp. | Active learning for defect classifier training |
| CN109961142B (zh) * | 2019-03-07 | 2023-05-12 | 腾讯科技（深圳）有限公司 | 一种基于元学习的神经网络优化方法及装置 |
-
2021
- 2021-05-17 WO PCT/CA2021/050672 patent/WO2021232149A1/en active Application Filing
- 2021-05-17 JP JP2023515224A patent/JP2023528688A/ja active Pending
- 2021-05-17 CA CA3166581A patent/CA3166581A1/en active Pending
- 2021-05-17 CN CN202180036832.7A patent/CN115668286A/zh active Pending
- 2021-05-20 TW TW110118315A patent/TW202203152A/zh unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023146946A1 (en) * | 2022-01-27 | 2023-08-03 | Te Connectivity Solutions Gmbh | Vision inspection system for defect detection |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115668286A (zh) | 2023-01-31 |
| TW202203152A (zh) | 2022-01-16 |
| JP2023528688A (ja) | 2023-07-05 |
| WO2021232149A1 (en) | 2021-11-25 |
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Legal Events
| Date | Code | Title | Description |
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| EEER | Examination request |
Effective date: 20220729 |
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| EEER | Examination request |
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