Application of phase-retrieval x-ray diffractometry to carbon doped SiGe(C)∕Si(C) superlattice structures. II. High resolution reconstruction using neural network root finder technique

2006 ◽  
Vol 99 (11) ◽  
pp. 113526 ◽  
Author(s):  
Ruben A. Dilanian ◽  
Andrei Y. Nikulin ◽  
Aliaksandr V. Darahanau ◽  
James Hester ◽  
Peter Zaumseil
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Phase Retrieval ◽  
X Ray ◽  
Carbon Doped ◽  
Superlattice Structures ◽  
High Resolution Reconstruction

Application of phase-retrieval x-ray diffractometry to carbon doped SiGe(C)/Si(C) superlattice structures

2003 ◽  
Vol 94 (2) ◽  
pp. 1007-1012 ◽  
Author(s):  
Karen K.-W. Siu ◽  
Andrei Y. Nikulin ◽  
Peter Zaumseil ◽  
Hiroshi Yamazaki ◽  
Tetsuya Ishikawa
Keyword(s):  
Phase Retrieval ◽  
X Ray ◽  
Carbon Doped ◽  
Superlattice Structures

scholarly journals Robust phase retrieval for high resolution edge illumination x-ray phase-contrast computed tomography in non-ideal environments

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Anna Zamir ◽  
Marco Endrizzi ◽  
Charlotte K. Hagen ◽  
Fabio A. Vittoria ◽  
Luca Urbani ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Phase Contrast ◽  
Phase Retrieval ◽  
X Ray

Application of Local Tomography Technique to High-Resolution Synchrotron X-Ray Imaging

2007 ◽  
Vol 539-543 ◽  
pp. 287-292 ◽  
Author(s):  
Tomomi Ohgaki ◽  
Hiroyuki Toda ◽  
Kentaro Uesugi ◽  
Toshiro Kobayashi ◽  
Koichi Makii ◽  
...  
Keyword(s):  
High Resolution ◽  
Sample Size ◽  
Small Region ◽  
X Ray ◽  
Local Tomography ◽  
X Ray Imaging ◽  
Resolution Observation ◽  
Element Number ◽  
High Resolution Reconstruction ◽  
Small Field Of View

X-ray CT method is a kind of nondestructive inspection, but has strong limitation in sample size due to a small field of view (FOV). The higher the resolution, the smaller FOV is, mainly due to the element number of available detectors commercially. Therefore, sample machining is more or less necessary so that the sample size is fit within the small FOV in the case of the high-resolution observation. Local tomography technique enables a high resolution reconstruction of small region of interests within a sample without the sample machining. In this study, we have evaluated the size effects of aluminum foam samples in terms of the 3D image quality by the local tomography techniques.

Application of sensitive, high-resolution imaging at a commercial lab-based X-ray micro-CT system using propagation-based phase retrieval

2017 ◽  
Vol 266 (2) ◽  
pp. 211-220 ◽  
Author(s):  
P. BIDOLA ◽  
K. MORGAN ◽  
M. WILLNER ◽  
A. FEHRINGER ◽  
S. ALLNER ◽  
...  
Keyword(s):  
High Resolution ◽  
Phase Retrieval ◽  
High Resolution Imaging ◽  
Micro Ct ◽  
X Ray ◽  
Resolution Imaging ◽  
Ct System

Determination of the defocus value of micrographs of ice-embedded specimen without carbon support film

1993 ◽  
Vol 51 ◽  
pp. 560-561
Author(s):  
Z. Hong Zhou
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Transfer Function ◽  
Carbon Support ◽  
Objective Lens ◽  
Contrast Transfer Function ◽  
X Ray ◽  
Spatial Frequencies ◽  
High Resolution Reconstruction

It is well recognized that the contrast transfer function (CTF) of an electron microscope modulates the image contrast The effects of this CTF are to reverse the sign of the phases and to alter the amplitudes at different spatial frequencies. These changes are dependent on the defocus of the objective lens in a given microscope setting. Therefore, it is necessary to determine the defocus experimentally in order to correct the phase reversal and the amplitudes due to the CTF for attaining a high resolution reconstruction. The most straightforward way of determining the defocus value is to determine the positions of the Thon rings in the CTF by optical or computed transforms. In a crystalline specimen, the defocus value of an image can be refined against the electron diffraction amplitude. For specimen of which the x-ray structure is known, one can also use the x-ray structure factor to determine the CTF parameters.

Neural network application to the inverse scattering problem in high-resolution X-ray diffraction

2001 ◽  
Vol 34 (3) ◽  
pp. 336-342 ◽  
Author(s):  
Christ Glorieux ◽  
Emil Zolotoyabko
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Inverse Scattering ◽  
Scattering Problem ◽  
Inverse Scattering Problem ◽  
X Ray Diffraction ◽  
Near Surface ◽  
X Ray ◽  
Neural Network Algorithm ◽  
Performance Statistics

The complicated inverse scattering problem of reconstructing depth-dependent lattice parameters from high-resolution X-ray diffraction spectra is analysed by using neural networks. Attention is paid to the practically important case of structural modifications in the near-surface layers of ion-implanted single crystals. The feasibility of a neural network algorithm is assessed on the basis of the performance statistics on a large number of simulated examples. The performance of the method on experimental data is tested using high-resolution X-ray diffraction spectra taken from He-implanted lithium niobate crystals.

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