scholarly journals A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method

Scanning ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lun Zhao ◽  
Yunlong Pan ◽  
Sen Wang ◽  
Liang Zhang ◽  
Md Shafiqul Islam
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Crack Detection ◽  
Rapid Development ◽  
Detection Accuracy ◽  
Learning Method ◽  
Sem Images ◽  
Bounding Boxes ◽  
Scanning Electron

The scanning electron microscope (SEM) is widely used in the analysis and research of materials, including fracture analysis, microstructure morphology, and nanomaterial analysis. With the rapid development of materials science and computer vision technology, the level of detection technology is constantly improving. In this paper, the deep learning method is used to intelligently identify microcracks in the microscopic morphology of SEM image. A deep learning model based on image level is selected to reduce the interference of other complex microscopic topography, and a detection method with dense continuous bounding boxes suitable for SEM images is proposed. The dense and continuous bounding boxes were used to obtain the local features of the cracks and rotating the bounding boxes to reduce the feature differences between the bounding boxes. Finally, the bounding boxes with filled regression were used to highlight the microcrack detection effect. The results show that the detection accuracy of our approach reached 71.12%, and the highest mIOU reached 64.13%. Also, microcracks in different magnifications and in different backgrounds were detected successfully.

scholarly journals Robust Deep-learning Based Autofocus Score Prediction for Scanning Electron Microscope

2020 ◽  
Vol 26 (S2) ◽  
pp. 702-705
Author(s):  
Hyun Jong Yang ◽  
Moohyun Oh ◽  
Jonggyu Jang ◽  
Hyeonsu Lyu ◽  
Junhee Lee
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Scanning Electron

Thick photoresists for electroforming metallic microcomponents

2008 ◽  
Vol 222 (1) ◽  
pp. 37-42 ◽  
Author(s):  
X Wei ◽  
C-H Lee ◽  
Z Jiang ◽  
K Jiang
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Current Paper ◽  
Sem Images ◽  
Advantages And Disadvantages ◽  
Uv Lithography ◽  
Electroforming Process ◽  
Electroformed Nickel ◽  
Scanning Electron

Recently, microelectroforming has been extensively applied to fabricating metallic components for sensors, actuators, and other systems. Thick photoresists are used for making micromoulds for electroforming and closely related to the quality and costs of an electroforming process. In the current paper, thick UV photoresists SU8, BPR100, and KMPR are analysed and compared in their electroforming performance of nickel microcomponents. Optimized UV lithography processes are introduced for producing micromoulds in each of the resists and scanning electron microscope (SEM) images of the moulds are presented and analysed. Then, electroformed nickel components from the micromoulds are presented. Finally, applicability of the photoresists to electroforming microcomponents is discussed. Each of the resists demonstrates advantages and disadvantages to suit different applications.

PET Depolymerization Catalyzed by Sulfates under Microwave Irradiation

2012 ◽  
Vol 550-553 ◽  
pp. 792-797 ◽  
Author(s):  
Wei Lu Zhang ◽  
Xiao Ni Shi ◽  
Xin Zhang ◽  
Chun Hua Han ◽  
Dong Zhang
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Microwave Irradiation ◽  
Polyethylene Terephthalate ◽  
Sem Images ◽  
Scanning Electron

Different sulfates were used as the catalysts of polyethylene terephthalate (PET) depolymerization under microwave of 250 watts, in which ZnSO4presented the best catalysis in this reaction, and the depolymerization degree (DPD) of PET was reached to 90 %. It was found that the depolymerization was occurred simultaneously on the surface and the internal parts of PET chips by the observation of scanning electron microscope (SEM) images. In addition, DPD increased with the improvement of the polarization forces of these sulfates.

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

2021 ◽  
Vol 11 (20) ◽  
pp. 9508
Author(s):  
Francisco López de la Rosa ◽  
Roberto Sánchez-Reolid ◽  
José L. Gómez-Sirvent ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Defect Detection ◽  
Scoping Review ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Future Research ◽  
Wide Range ◽  
Scanning Electron

Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. 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 and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

scholarly journals QUALITY REINFORCEMENT OF ELECTROPLATING ZINC COATINGSELECTRODEPOSITED FROM CYANIDE FREE ALKALINE SOLUTION BY POLYAMINE 70.000 AND POLYVINYL ALCOHOL 16.000

2018 ◽  
Vol 55 (5B) ◽  
pp. 18
Author(s):  
Truong Thi Nam
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Polyvinyl Alcohol ◽  
Zinc Coating ◽  
Alkaline Solutions ◽  
Zinc Ions ◽  
Sem Images ◽  
Zinc Coatings ◽  
Scanning Electron ◽  
Good Agreement

Zinc coatings have been deposited electrochemically from cyanine free alkaline solutions containing zinc ions with the presence of polyamine 70.000 and polyvinyl alcohol at different contents. The scanning electron microscope (SEM) images showed that the size of zinc grains decreased with the presence of polyamine 70.000 and polyvinyl alcohol with smoother surface of zinc coating. The polarization measurements also revealed that the coatings with the presence of polyamine or polyvinyl alcohol possessed higher value of polarity degree. This result is in good agreement with the result obtained from SEM images.

Performance enhancement of Scanning Electron Microscope using a Deep Convolutional Neural Network

2022 ◽  
Author(s):  
Suresh Panchal ◽  
Unnikrishnan Gopinathan ◽  
Suwarna Datar
Keyword(s):  
Neural Network ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Convolutional Neural Network ◽  
Performance Enhancement ◽  
Deep Convolutional Neural Network ◽  
Sem Images ◽  
Scan Rate ◽  
Charging Effect ◽  
Scanning Electron

Abstract We report noise reduction and image enhancement in Scanning Electron Microscope (SEM) imaging while maintaining a Fast-Scan rate during imaging, using a Deep Convolutional Neural Network (D-CNN). SEM images of non-conducting samples without conducting coating always suffer from charging phenomenon, giving rise to SEM images with low contrast or anomalous contrast and permanent damage to the sample. One of the ways to avoid this effect is to use Fast-Scan mode, which suppresses the charging effect fairly well. Unfortunately, this also introduces noise and gives blurred images. The D-CNN has been used to predict relatively noise-free images as obtained from a Slow-Scan from a noisy, Fast-Scan image. The predicted images from D-CNN have the sharpness of images obtained from a Slow-Scan rate while reducing the charging effect due to images obtained from Fast-Scan rates. We show that using the present method, and it is possible to increase the scanning rate by a factor of about seven with an output of image quality comparable to that of the Slow-Scan mode. We present experimental results in support of the proposed method.

Analyze of scanning electron microscope images using deep learning for calcium carbonate polymorph detection

2020 ◽  
Vol 20 (2020) ◽  
pp. 416-417
Author(s):  
Caio Marcellos ◽  
Amaro Gomes Barreto Jr ◽  
Juliana Braga Rodrigues Loureiro ◽  
Elvis do Amaral Soares ◽  
Danilo Naiff ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electron Microscope ◽  
Calcium Carbonate ◽  
Scanning Electron Microscope ◽  
Microscope Images ◽  
Scanning Electron
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