Non-destructive thickness characterisation of 3D multilayer semiconductor devices using optical spectral measurements and machine learning

2020 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Hyunsoo Kwak ◽  
◽  
Sungyoon Ryu ◽  
Suil Cho ◽  
Junmo Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semiconductor Devices ◽  
Spectral Measurements ◽  
Non Destructive

scholarly journals A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2285 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski
Keyword(s):  
Machine Learning ◽  
Spatial Analysis ◽  
Building Materials ◽  
Supervised Machine Learning ◽  
Electrical Tomography ◽  
High Processing Speed ◽  
Image Pixels ◽  
Architectural Significance ◽  
Building Walls ◽  
Non Destructive

This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls as both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

The Analyst ◽  
2021 ◽  
Author(s):  
Danuta Liberda ◽  
Paulina Koziol ◽  
Magda K. Raczkowska ◽  
Wojciech M. Kwiatek ◽  
Tomasz P. Wrobel
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Histological Classification ◽  
Spectral Quality ◽  
Interference Effects ◽  
Ir Imaging ◽  
Ft Ir ◽  
Non Destructive

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms regardless of the presence of interference effects obscuring the spectra.

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