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A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

Francisco López de la Rosa, Roberto Sánchez-Reolid, José L. Gómez-Sirvent, Rafael Morales, Antonio Fernández-Caballero
2021 Applied Sciences  
Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection  ...  Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications.  ...  Finally, the two existing approaches for carrying out defect detection and classification will be discussed. Elements of a CNN Neurons The neuron is the smallest element on a CNN.  ... 
doi:10.3390/app11209508 fatcat:ro5opdzcbfaormjhwgzrasnqwy

State of the Art in Defect Detection Based on Machine Vision

Zhonghe Ren, Fengzhou Fang, Ning Yan, You Wu
2021 International Journal of Precision Engineering and Manufacturing - Green Technology  
In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images.  ...  In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed.  ...  For supervised steel defect classification, Masci et al. [178] presented a max-pooling CNN approach.  ... 
doi:10.1007/s40684-021-00343-6 fatcat:gzukmfsx3veexktu2mg4tcbdfi

Visual assessment of multi-photon interference

Fulvio Flamini, Nicolò Spagnolo, Fabio Sciarrino
2019 Quantum Science and Technology  
We envisage that this approach will inspire further theoretical investigations, for instance for a reliable assessment of quantum computational advantage.  ...  As a notable example, t-distributed Stochastic Neighbor Embedding (t-SNE) represents the state of the art for visualization of data sets of large dimensionality.  ...  To investigate the efficacy of CNNs for validating multi-particle interference based on t-SNE embeddings, we have simulated several experiments for n=4 and various m.  ... 
doi:10.1088/2058-9565/ab04fc fatcat:nju4tryhufbttiwzbcwnr6kxuq

Research Progress of Visual Inspection Technology of Steel Products—A Review

Xiaohong Sun, Jinan Gu, Shixi Tang, Jing Li
2018 Applied Sciences  
The network framework based on deep learning provides space for the development of end-to-end mode inspection technology, which would greatly promote the implementation of intelligent manufacturing.  ...  A major breakthrough in this field can be achieved if sufficient research regarding visual inspection technologies is undertaken.  ...  For hot rolled steel, the strip itself is a luminous heating element.  ... 
doi:10.3390/app8112195 fatcat:g2cl62arh5bnlg76fesmldhlja

Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series [article]

Mahdyar Ravanbakhsh
2019 arXiv   pre-print
Novelty detection is a process for distinguishing the observations that differ in some respect from the observations that the model is trained on.  ...  Therefore, detecting the Novel classes in unsupervised and semi-supervised settings is a crucial step in such tasks.  ...  Their dedication to my education provided the foundation for my studies.  ... 
arXiv:1904.04741v1 fatcat:fdwhsuaoi5hcdbjzcbjh2z6ydu

On the Use of Robots and Vision Technologies for the Inspection of Vessels: a Survey on Recent Advances

Francisco Bonnin-Pascual, Alberto Ortiz
2019 Zenodo  
This paper surveys approaches which can contribute to the reengineering process of vessel visual inspection focusing on two main aspects: robotic platforms which can be used for the visual inspection of  ...  To prevent major damage / accidents, intensive inspection schemes must be carried out periodically, identifying the affected plates for a subsequent repair / replacement.  ...  On the other side, 706 the CNN is implemented using a pre-trained model based on AlexNet (Krizhevsky et al., 2012).  ... 
doi:10.5281/zenodo.4253168 fatcat:g4hfbverjjhnxgpcpjp2m2nvbm

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Abd Al Rahman M. Abu Ebayyeh, Alireza Mousavi
2020 IEEE Access  
Due to its efficiency and accuracy, a sparse optical flow algorithm called Lucas-Kanade were used for on-line defect inspection in manufacturing.  ...  The output for CNN-2 were used as an input for a third CNN (called CNN-3). The outputs for both CNN-1 and CNN-3 were used for classification of the SMT solder joint (Good or bad).  ...  The new keywords are: • "Optical Inspection" AND "LED" • "Optical Inspection" AND "Wafer" • "Optical Inspection" AND "PCB" • "Optical Inspection" AND "FPD" Manual searching technique were also conducted  ... 
doi:10.1109/access.2020.3029127 fatcat:hoimi667cndsrimsnwasamtdey

The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z~1.5

E. Koulouridis, N. Clerc, T. Sadibekova, M. Chira, E. Drigga, et al.
2021 Astronomy and Astrophysics  
X-ray cluster surveys are well suited for this purpose because they are far less affected by projection effects than optical surveys, and cluster properties can be predicted with good accuracy. Aims.  ...  Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information.  ...  Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S.  ... 
doi:10.1051/0004-6361/202140566 fatcat:ip47qkpfa5concww36uxvrjt4u

The X-CLASS survey: A catalogue of 1646 X-ray-selected galaxy clusters up to z∼1.5 [article]

E. Koulouridis, N. Clerc, T. Sadibekova, M. Chira, E. Drigga, L. Faccioli, J. P. Le Fèvre, C. Garrel, E. Gaynullina, A. Gkini, M. Kosiba, F. Pacaud (+16 others)
2021 arXiv   pre-print
X-ray cluster surveys are well suited for this purpose, since they are far less affected than optical surveys by projection effects, and cluster properties can be predicted with good accuracy.  ...  Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information.  ...  Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S.  ... 
arXiv:2104.06617v2 fatcat:nflusy4ntrdalmtprcs52rm53i

2020 Index IEEE Transactions on Instrumentation and Measurement Vol. 69

2020 IEEE Transactions on Instrumentation and Measurement  
Oct. 2020 7530-7541 Hernandez, A., see Aparicio-Esteve, E., TIM Aug. 2020 5589-5603 Hernandez, H., de Souza Sanches, B.C., Carvalho, D., Bregant, M  ...  Meenalochani, M., and Sudha, S., Influence of Received Signal Strength on Prediction of Cluster Head and Number of Rounds; TIM June 2020 3739-3749 Hendeby, G., see Kasebzadeh, P., TIM Aug. 2020 5862  ...  Samore, A., +, TIM Sept. 2020 6766-6775 Deep Multimodel Cascade Method Based on CNN and Random Forest for Pharmaceutical Particle Detection.  ... 
doi:10.1109/tim.2020.3042348 fatcat:a5f4fsqs45fbbetre6zwsg3dly

Urban Flood Mapping with Bi-temporal Multispectral Imagery via a Self-supervised Learning Framework

Bo Peng, Qunying Huang, Jamp Vongkusolkit, Song Gao, Daniel B. Wright, Zheng N. Fang, Yi Qiang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This article proposes a self-supervised learning framework for patch-wise urban flood mapping using bitemporal multispectral satellite imagery.  ...  Patch-wise change vector analysis is used with patch features learned through a self-supervised autoencoder to produce patch-wise change maps showing potentially flood-affected areas.  ...  Self-supervised learning is a special type of unsupervised learning [26] , [27] .  ... 
doi:10.1109/jstars.2020.3047677 fatcat:lizyhbvitzbcfb36w4eujg2vti

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

Chih-Wen Chang, Hau-Wei Lee, Chein-Hung Liu
2018 Inventions  
scheme for real-time object detection [25], a deep CNN based regression scheme for the estimation of remaining useful life [26], a deep CNN approach to automated feature extraction in industrial inspection  ...  image databases [18], the CNN method for house number digit classification [19], the deep CNN approach to Imagenet classification [20], a CNN scheme for a hybrid nn-hmm model for speech recognition [21  ...  A self-organizing, self-adjusting fuzzy model was proposed for multi-sensor integration.  ... 
doi:10.3390/inventions3030041 fatcat:6qrwhmrl2bfwrgmovqvsyx5p3y

Identification of new particle formation events with deep learning

Jorma Joutsensaari, Matthew Ozon, Tuomo Nieminen, Santtu Mikkonen, Timo Lähivaara, Stefano Decesari, M. Cristina Facchini, Ari Laaksonen, Kari E. J. Lehtinen
2018 Atmospheric Chemistry and Physics  
The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types  ...  We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification.  ...  Junninen et al. (2007) introduced an automatic algorithm based on self-organizing maps (SOMs) and a decision tree to classify aerosol size distributions.  ... 
doi:10.5194/acp-18-9597-2018 fatcat:2tdqisczsbaq5jwatvar7q6pyu

A Robust Localization System for Inspection Robots in Sewer Networks

David Alejo, Fernando Caballero, Luis Merino
2019 Sensors  
To this end, this paper presents a robust localization system for global pose estimation on sewers.  ...  The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage.  ...  for their logistic support during the field tests.  ... 
doi:10.3390/s19224946 pmid:31766253 pmcid:PMC6891562 fatcat:k46er7sfnzehbcu2egdkoch7wu

Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review [article]

Hassan Raji, Muhammad Tayyab, Jianye Sui, Seyed Reza Mahmoodi, Mehdi Javanmard
2021 arXiv   pre-print
Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics.  ...  Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care  ...  However, to be more precise they are self-supervised because they generate their own labels from the training data.  ... 
arXiv:2101.01866v1 fatcat:rws7k3yp6ndmnlkqcvafmkgphi
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