Wang et al., 2022 - Google Patents
Attention-based deep learning for chip-surface-defect detectionWang et al., 2022
- Document ID
- 11433749708944586972
- Author
- Wang S
- Wang H
- Yang F
- Liu F
- Zeng L
- Publication year
- Publication venue
- The International Journal of Advanced Manufacturing Technology
External Links
Snippet
Unlike objects (such as cats and dogs) in the ImageNet, the surface defects on chips have a relatively tiny defect areas, yet they contain a large amount of information. The traditional deep learning methods have unsatisfied performance for tiny defects. Therefore, we …
- 238000001514 detection method 0 title abstract description 67
Classifications
-
- G—PHYSICS
- G06—COMPUTING; 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; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- G—PHYSICS
- G06—COMPUTING; 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; 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
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; 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
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K2209/19—Recognition of objects for industrial automation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Attention-based deep learning for chip-surface-defect detection | |
Zhou et al. | Review of vision-based defect detection research and its perspectives for printed circuit board | |
Wu et al. | Solder joint recognition using mask R-CNN method | |
CN109829903B (en) | Chip surface defect detection method based on convolution denoising autoencoder | |
Zheng et al. | HLU 2-Net: a residual U-structure embedded U-Net with hybrid loss for tire defect inspection | |
Li et al. | An end-to-end defect detection method for mobile phone light guide plate via multitask learning | |
Meng et al. | Visual inspection of aircraft skin: Automated pixel-level defect detection by instance segmentation | |
CN115136209A (en) | Defect detection system | |
Lian et al. | Automatic visual inspection for printed circuit board via novel Mask R-CNN in smart city applications | |
Yang et al. | A scratch detection method based on deep learning and image segmentation | |
CN114417993A (en) | Scratch detection method based on deep convolutional neural network and image segmentation | |
CN113807378A (en) | Training data increment method, electronic device and computer readable recording medium | |
Ma et al. | A hierarchical attention detector for bearing surface defect detection | |
Zheng et al. | LED chip defect detection method based on a hybrid algorithm | |
Noroozi et al. | Towards Optimal Defect Detection in Assembled Printed Circuit Boards Under Adverse Conditions | |
Singh et al. | Performance analysis of object detection algorithms for robotic welding applications in planar environment | |
Wang et al. | Conditional TransGAN‐Based Data Augmentation for PCB Electronic Component Inspection | |
Lv et al. | LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces | |
Zhang et al. | An automatic defect detection method for TO56 semiconductor laser using deep convolutional neural network | |
Hu et al. | Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism. | |
Juang et al. | Inspection of lead frame defects using deep CNN and cycle-consistent GAN-based defect augmentation | |
Wu et al. | Semiautomatic mask generating for electronics component inspection | |
Li et al. | Container damage identification based on Fmask-RCNN | |
Zhong et al. | Detection of oxidation region of flexible integrated circuit substrate based on topology mapping | |
Zhao et al. | Online assembly inspection integrating lightweight hybrid neural network with positioning box matching |