scholarly journals Shape- and Element-Sensitive Reconstruction of Periodic Nanostructures with Grazing Incidence X-ray Fluorescence Analysis and Machine Learning

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1647
Author(s):  
Anna Andrle ◽  
Philipp Hönicke ◽  
Grzegorz Gwalt ◽  
Philipp-Immanuel Schneider ◽  
Yves Kayser ◽  
...  

The characterization of nanostructured surfaces with sensitivity in the sub-nm range is of high importance for the development of current and next-generation integrated electronic circuits. Modern transistor architectures for, e.g., FinFETs are realized by lithographic fabrication of complex, well-ordered nanostructures. Recently, a novel characterization technique based on X-ray fluorescence measurements in grazing incidence geometry was proposed for such applications. This technique uses the X-ray standing wave field, arising from an interference between incident and the reflected radiation, as a nanoscale sensor for the dimensional and compositional parameters of the nanostructure. The element sensitivity of the X-ray fluorescence technique allows for a reconstruction of the spatial element distribution using a finite element method. Due to a high computational time, intelligent optimization methods employing machine learning algorithms are essential for timely provision of results. Here, a sampling of the probability distributions by Bayesian optimization is not only fast, but it also provides an initial estimate of the parameter uncertainties and sensitivities. The high sensitivity of the method requires a precise knowledge of the material parameters in the modeling of the dimensional shape provided that some physical properties of the material are known or determined beforehand. The unknown optical constants were extracted from an unstructured but otherwise identical layer system by means of soft X-ray reflectometry. The spatial distribution profiles of the different elements contained in the grating structure were compared to scanning electron and atomic force microscopy and the influence of carbon surface contamination on the modeling results were discussed. This novel approach enables the element sensitive and destruction-free characterization of nanostructures made of silicon nitride and silicon oxide with sub-nm resolution.

2020 ◽  
Vol 1004 ◽  
pp. 393-400
Author(s):  
Tuerxun Ailihumaer ◽  
Hongyu Peng ◽  
Balaji Raghothamachar ◽  
Michael Dudley ◽  
Gilyong Chung ◽  
...  

Synchrotron monochromatic beam X-ray topography (SMBXT) in grazing incidence geometry shows black and white contrast for basal plane dislocations (BPDs) with Burgers vectors of opposite signs as demonstrated using ray tracing simulations. The inhomogeneous distribution of these dislocations is associated with the concave/convex shape of the basal plane. Therefore, the distribution of these two BPD types were examined for several 6-inch diameter 4H-SiC substrates and the net BPD density distribution was used for evaluating the nature and magnitude of basal plane bending in these wafers. Results show different bending behaviors along the two radial directions - [110] and [100] directions, indicating the existence of non-isotropic bending. Linear mapping of the peak shift of the 0008 reflection along the two directions was carried out using HRXRD to correlate with the results from the SMBXT measurements. Basal-plane-tilt angle calculated using the net BPD density derived from SMBXT shows a good correlation with those obtained from HRXRD measurements, which further confirmed that bending in basal plane is caused by the non-uniform distribution of BPDs. Regions of severe bending were found to be associated with both large tilt angles (95% black contrast BPDs to 5% white contrast BPDs) and abrupt changes in a and c lattice parameters i.e. local strain.


2015 ◽  
Vol 212 (3) ◽  
pp. 523-528 ◽  
Author(s):  
Philipp Hönicke ◽  
Blanka Detlefs ◽  
Matthias Müller ◽  
Erik Darlatt ◽  
Emmanuel Nolot ◽  
...  

Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.


2009 ◽  
Vol 24 (6) ◽  
pp. 792 ◽  
Author(s):  
Alex von Bohlen ◽  
Markus Krämer ◽  
Christian Sternemann ◽  
Michael Paulus

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