An effective method of contour extraction for SEM image based on DCNN

2020 ◽  
Author(s):  
Tao Zhou ◽  
Xuelong Shi ◽  
YanYan ◽  
Chen Li ◽  
Shoumian Chen ◽  
...  
Keyword(s):  
Contour Extraction ◽  
Sem Image

scholarly journals Fast and Robust DCNN Based Lithography SEM Image Contour Extraction Models

2021 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Tao Zhou ◽  
◽  
Xuelong Shi ◽  
Chen Li ◽  
Yan Yan ◽  
...  
Keyword(s):  
Contour Extraction ◽  
Sem Image ◽  
Image Contour

Accurate contour extraction from mask SEM image

2014 ◽  
Author(s):  
Izumi Santo ◽  
Akira Higuchi ◽  
Mirai Anazawa ◽  
Hideaki Bandoh
Keyword(s):  
Contour Extraction ◽  
Sem Image

A Color Coded STEM/SEM Image Storage System

1981 ◽  
Vol 39 ◽  
pp. 272-273
Author(s):  
Y. Kokubo ◽  
W. H. Hardy ◽  
J. Dance ◽  
K. Jones
Keyword(s):  
Image Processing ◽  
Electron Beam ◽  
Storage System ◽  
Particle Analysis ◽  
Specific Information ◽  
X Rays ◽  
Sem Image ◽  
Backscattered Electrons ◽  
Wide Range ◽  
Scanning Electron

A color coded digital image processing is accomplished by using JEM100CX TEM SCAN and ORTEC’s LSI-11 computer based multi-channel analyzer (EEDS-II-System III) for image analysis and display. Color coding of the recorded image enables enhanced visualization of the image using mathematical techniques such as compression, gray scale expansion, gamma-processing, filtering, etc., without subjecting the sample to further electron beam irradiation once images have been stored in the memory.The powerful combination between a scanning electron microscope and computer is starting to be widely used 1) - 4) for the purpose of image processing and particle analysis. Especially, in scanning electron microscopy it is possible to get all information resulting from the interactions between the electron beam and specimen materials, by using different detectors for signals such as secondary electron, backscattered electrons, elastic scattered electrons, inelastic scattered electrons, un-scattered electrons, X-rays, etc., each of which contains specific information arising from their physical origin, study of a wide range of effects becomes possible.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Outreach Opportunities Using the Instructional SEM at Iowa State University

1996 ◽  
Vol 54 ◽  
pp. 412-413
Author(s):  
L. S. Chumbley ◽  
M. Meyer ◽  
K. Fredrickson ◽  
F.C. Laabs
Keyword(s):  
Materials Science ◽  
State University ◽  
Digital Cameras ◽  
Audio Signals ◽  
Iowa State University ◽  
Cornell University ◽  
Sem Image ◽  
Local Schools ◽  
Materials Science And Engineering ◽  
Video And Audio

The development of a scanning electron microscope (SEM) suitable for instructional purposes has created a large number of outreach opportunities for the Materials Science and Engineering (MSE) Department at Iowa State University. Several collaborative efforts are presently underway with local schools and the Department of Curriculum and Instruction (C&I) at ISU to bring SEM technology into the classroom in a near live-time, interactive manner. The SEM laboratory is shown in Figure 1.Interactions between the laboratory and the classroom use inexpensive digital cameras and shareware called CU-SeeMe, Figure 2. Developed by Cornell University and available over the internet, CUSeeMe provides inexpensive video conferencing capabilities. The software allows video and audio signals from Quikcam™ cameras to be sent and received between computers. A reflector site has been established in the MSE department that allows eight different computers to be interconnected simultaneously. This arrangement allows us to demonstrate SEM principles in the classroom. An Apple Macintosh has been configured to allow the SEM image to be seen using CU-SeeMe.

SEM image sharpness analysis

1996 ◽  
Vol 54 ◽  
pp. 142-143
Author(s):  
M. T. Postek ◽  
A. E. Vladar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Quantitative Information ◽  
Primary Electron ◽  
Image Sharpness ◽  
Critical Dimensions ◽  
Sem Image ◽  
Frequency Changes ◽  
Electron Microscopes ◽  
Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

Preparation and In-vitro Evaluation of Fe3O4 Encapsulated by Chitosan Loaded Capecitabine Nanoparticles for the Treatment of Breast Cancer

2016 ◽  
Vol 6 (3) ◽  
Author(s):  
Shanmuganathan S. ◽  
Nigma S. ◽  
Anbarasan B. ◽  
Harika B.
Keyword(s):  
Fe3o4 Nanoparticles ◽  
Polymer Concentration ◽  
In Vitro Release ◽  
Entrapment Efficiency ◽  
In Vitro Evaluation ◽  
Sem Image ◽  
Therapeutic Molecules ◽  
Dialysis Method ◽  
Vitro Release

Nanoparticulate Carriers which is biodegradable, biocompatible and bio adhesive have significant feasible applications for administration of therapeutic molecules. The present study was aimed to formulate and optimise Capecitabine loaded Chitosan-Fe3O4 Nanoparticles and to study the in-vitro evaluation by sigma dialysis method. Capecitabine loaded chitosan – Fe3O4 nanoparticles batches with different ratios of drug: polymer (1:1, 1:2, 1:3, 1:4, 1:5, 1:6) were prepared by ionic gelation method. Increase in polymer concentration increases the nanoparticle drug content. Entrapment efficiency was 60.12% with drug to polymer ratio F3 (1:3). In-vitro release was found to be 65.20% for 12 hrs. Capecitabine from chitosanFe3O4 nanoparticles SEM image reveals discrete spherical structure and particles with size range of 100-500nm. FTIR studies represent the functional groups present with no characteristics change in formulations. Samples stored at refrigerator conditions showed better stability compared with samples kept at other conditions during 8 weeks of storage.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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