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SUMMARY:The Application of Advanced X-Ray Diffraction Image Processing Met
 hods for Study Linear Defects in Silicon Single Crystals
DTSTART;VALUE=DATE-TIME:20200715T100000Z
DTEND;VALUE=DATE-TIME:20200715T102000Z
DTSTAMP;VALUE=DATE-TIME:20260411T142418Z
UID:indico-contribution-1024@indico.inp.nsk.su
DESCRIPTION:Speakers: Denis Zolotov (Shubnikov Institute of Crystallograph
 y FSRC “Crystallography and Photonics” RAS)\, Irina Dyachkova (FSRC "C
 rystallography and Photonics" RAS)\nThe visualization and analysis of 3D f
 ields of elastic displacements of micro-dimensional defects and dislocatio
 n structures in the volume of single crystals are important in the develop
 ment of new functional materials. For this purpose\, the X-ray diffraction
  tomography (XRDT) method is increasingly being used. We use XRDT to study
  the real structure of different single crystals\, in particular\, to visu
 alize the spatial location of dislocation half-loops in Si (111) single cr
 ystal obtained by a four-point bending method [1]. The currently used math
 ematical algorithms of XRDT data processing are the evolution of absorptio
 n tomography methods. Thus\, there is a motivation to modify already exist
 ing algorithms for processing experimental results in order to develop new
  mathematical software based on them for modeling images of micro-dimensio
 nal defects in crystals. The tools of X-ray diffraction theory\, as well a
 s modern methods of digital image processing can be used for interpretatio
 n of the obtained data.   \n  \nThe specific feature of XRDT measurements 
 is the impossibility to register a direct beam or its analogue (flatfield 
 correction)\, which could be used to correct the background of the resulti
 ng projections. In this work\, we propose a statistical method of analyzin
 g diffraction projections to separate the noise component of the backgroun
 d (scattered radiation\, dark current of detector\, etc.) from the useful 
 signal.   \n  \nIn particular\, an approach using antialiasing the backgro
 und signal with the Hamming's kernel in a 2D implementation has been propo
 sed. It is proposed to use an algorithm for statistical recognition using 
 Kendall’s rank correlation criterion to recognize the boundaries and pea
 ks in the images. Kendall’s statistic and the concordation coefficient a
 re calculated inside the scan window of the specified width. In this case\
 , only image trends\, i.e. relative intensity values\, are compared.   \n 
  \nThe results of filtration depend to a large extent on the accuracy of n
 oise dispersion estimation in the raw data. The main quality criterion of 
 the solution is the value of the residual autocorrelation\, which should c
 orrespond to a sample from a random sequence. The Durbin-Watson autocorrel
 ation criterion [2] and several semi-empirical criteria based on the analy
 sis of the curvature of the smoothed curve and the relative value of the s
 ystematic component in the residues were chosen as the estimation.   \n  \
 nThe application of developed algorithms and software for effective automa
 tic noise filtering and smoothing of 2D diffraction projections using the 
 criteria of difference autocorrelation significantly improves 3D reconstru
 ction result of the dislocation half-loops in Si (111) single crystal.   \
 n  \nThis work was supported by Russian Foundation for Basic Research (pro
 ject 19-02-00556 A) in the part of image processing and the Ministry of Sc
 ience and Higher Education within the State assignment FSRC “Crystallogr
 aphy and Photonics” RAS in part of applying tomography algorithms.   \nR
 eferences   \n1\\. V. Asadchikov\, A. Buzmakov\, F. Chukhovskii et al\, J.
  Appl. Cryst. 51\, 1616 (2018).   \n2\\. J. Durbin\, Biometrika 58\, 1 (19
 71).\n\nhttps://indico.inp.nsk.su/event/24/contributions/1024/
LOCATION: Zoom 890 9721 5207
URL:https://indico.inp.nsk.su/event/24/contributions/1024/
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