Deep learning for automated focus quality detection in wafer inspection

2021 ◽  
Author(s):  
Carrie Wright ◽  
Samuel J. Yang
Keyword(s):  
Deep Learning ◽  
Wafer Inspection ◽  
Quality Detection

scholarly journals Assembly Quality Detection Based on Class-Imbalanced Semi-Supervised Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 10373
Author(s):  
Zichen Lu ◽  
Jiabin Jiang ◽  
Pin Cao ◽  
Yongying Yang
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Class Imbalance ◽  
Loss Functions ◽  
Quality Inspection ◽  
Data Annotation ◽  
Quality Detection ◽  
High Classification Accuracy ◽  
Total Data ◽  
The Mean

Due to the imperfect assembly process, the unqualified assembly of a missing gasket or lead seal will affect the product’s performance and possibly cause safety accidents. Machine vision method based on deep learning has been widely used in quality inspection. Semi-supervised learning (SSL) has been applied in training deep learning models to reduce the burden of data annotation. The dataset obtained from the production line tends to be class-imbalanced because the assemblies are qualified in most cases. However, most SSL methods suffer from lower performance in class-imbalanced datasets. Therefore, we propose a new semi-supervised algorithm that achieves high classification accuracy on the class-imbalanced assembly dataset with limited labeled data. Based on the mean teacher algorithm, the proposed algorithm uses certainty to select reliable teacher predictions for student learning dynamically, and loss functions are modified to improve the model’s robustness against class imbalance. Results show that when only 10% of the total data are labeled, and the imbalance rate is 5.3, the proposed method can improve the accuracy from 85.34% to 93.67% compared to supervised learning. When the amount of annotated data accounts for 20%, the accuracy can reach 98.83%.

scholarly journals An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 80
Author(s):  
Sai Xu ◽  
Huazhong Lu ◽  
Christopher Ference ◽  
Qianqian Zhang
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Data Processing ◽  
Water Content ◽  
Information Fusion ◽  
Nir Spectroscopy ◽  
Internal Quality ◽  
Source Information ◽  
Accuracy Improvement ◽  
Quality Detection

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

scholarly journals Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1001 ◽  
Author(s):  
Guixiong Liu ◽  
Binyuan He ◽  
Siyuang Liu ◽  
Jian Huang
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Detection Methods ◽  
Data Sets ◽  
Learning Method ◽  
Assembly Quality ◽  
Quality Detection ◽  
Detection And Identification ◽  
Industrial Assembly ◽  
Instance Segmentation

Chassis assembly quality is a necessary step to improve product quality and yield. In recent years, with the continuous expansion of deep learning method, its application in product quality detection is increasingly extensive. The current limitations and shortcomings of existing quality detection methods and the feasibility of improving the deep learning method in quality detection are presented and discussed in this paper. According to the characteristics of numerous parts and complex types of chassis assembly components, a method for chassis assembly detection and identification based on deep learning component segmentation is proposed. In the proposed method, assembly quality detection is first performed using the Mask regional convolutional neural network component instance segmentation method, which reduces the influence of complex illumination conditions and background detection. Next, a standard dictionary of chassis assembly is built, which is connected with Mask R-CNN in a cascading way. The component mask is obtained through the detection result, and the component category and assembly quality information is extracted to realize chassis assembly detection and identification. To evaluate the proposed method, an industrial assembly chassis was used to create datasets, and the method is effective in limited data sets of industrial assembly chassis. The experimental results indicate that the accuracy of the proposed method can reach 93.7%. Overall, the deep learning method realizes complete automation of chassis assembly detection.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning
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