Machine Learning Prediction of Defect Structures in Amorphous Silicon Dioxide

2021 ◽  
Author(s):  
Diego Milardovich ◽  
Markus Jech ◽  
Dominic Waldhoer ◽  
Al-Moatasem Bellah El-Sayed ◽  
Tibor Grasser
Keyword(s):  
Machine Learning ◽  
Silicon Dioxide ◽  
Amorphous Silicon ◽  
Defect Structures ◽  
Amorphous Silicon Dioxide

scholarly journals Сравнительный фотолюминесцентный анализ точечных дефектов в SiO-=SUB=-2-=/SUB=-, индуцированных имплантацией ионов Ar-=SUP=-+-=/SUP=- и облучением нейтронами

2019 ◽  
Vol 45 (5) ◽  
pp. 24
Author(s):  
И.П. Щербаков ◽  
А.Е. Чмель
Keyword(s):  
Silicon Dioxide ◽  
Amorphous Silicon ◽  
Point Defects ◽  
Photoluminescence Spectrum ◽  
Thermal Neutrons ◽  
Optical Transitions ◽  
Atomic Collisions ◽  
Amorphous Silicon Dioxide ◽  
Spectral Bands ◽  
Atomic Nuclei

AbstractThe introduction of Si^+ ions and ions of other elements into amorphous silicon dioxide during their interaction causes damage to the structural bonds, which is observed in the vibrational spectral bands. Pure SiO_2 has no optical transitions but the bands of induced point defects appear in the photoluminescence spectrum when ions/neutrons are introduced. The generation of photoluminescence-active defects by fluxes of Ar^+ ion and thermal neutrons is compared. It is shown that the nature of damage to the structure is associated with both the specifics of the synthesis/processing of the material and the features of the interaction between the substance and ions (atomic collisions) and neutrons (collisions with atomic nuclei).

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