Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2001 - 20104

Settings

COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Germanium

Search Results

Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 10
    Citation - Scopus: 10
    Evolution of Sio2/Ge Multilayer Structure During High Temperature Annealing
    (Elsevier Ltd., 2010) Şahin, D.; Yıldız, İlker; Gençer İmer, Arife; Aygün, Gülnur; Slaoui, A.; Turan, Raşit
    Use of germanium as a storage medium combined with a high-k dielectric tunneling oxide is of interest for non-volatile memory applications. The device structure consists of a thin HfO2 tunneling oxide with a Ge layer either in the form of continuous layer or discrete nanocrystals and relatively thicker SiO2 layer functioning as a control oxide. In this work, we studied interface properties and formation kinetics in SiO2/Ge/HfO2(Ge) multilayer structure during deposition and annealing. This material structure was fabricated by magnetron sputtering and studied by depth profiling with XPS and by Raman spectroscopy. It was observed that Ge atoms penetrate into HfO2 layer during the deposition and segregate out with annealing. This is related to the low solubility of Ge in HfO2 which is observed in other oxides as well. Therefore, Ge out diffusion might be an advantage in forming well controlled floating gate on top of HfO2. In addition we observed the Ge oxidation at the interfaces, where HfSiOx formation is also detected. © 2009 Elsevier B.V. All rights reserved.
  • Article
    Citation - WoS: 12
    Citation - Scopus: 12
    Ge Nanocrystals Embedded in Sio2 in Mos Based Radiation Sensors
    (Elsevier Ltd., 2010) Aktağ, Aliekber; Yılmaz, Ercan; Mogaddam, Nader A.P.; Aygün, Gülnur; Cantaş, Ayten; Turan, Raşit
    In this work, the effects of gamma radiation on the Raman spectra of Ge nanocrystals embedded in SiO2 have been investigated. SiO2 films containing nanoparticles of Ge were grown using the r.f.-magnetron sputtering technique. Formation of Ge nanocrystals was observed after high temperature annealing in an inert atmosphere and confirmed by Raman measurements. The intensity of the Raman signal originating from Ge nanocrystals was found to decrease with increasing gamma radiation. The study also includes the gamma radiation effects on MOS structure with Ge nanocrystals embedded in SiO2. The gamma radiation effects from 500 up to 4000 Gray were investigated. Capacitance-voltage measurements were performed and analyzed. Oxide traps and interface trap charges were calculated. Results show that MOS structure with Ge nanocrystals embedded in SiO2 is a good candidate to be used in radiation sensors, especially at high radiation doses. © 2010 Elsevier B.V. All rights reserved.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Evaluation of Dissolution Methods in the Presence of High Chloride Content for the Determination of Germanium in Geological Matrices by Hydride Generation Atomic Absorption Spectrometry
    (Japan Society for Analytical Chemistry, 2001) Abbasi, Husam U.; Eroğlu, Ahmet Emin; Anwari, Mohammad A.; Volkan, Mürvet
    The sample dissolution is probably the most crucial step in the determination of germanium in geological and metallurgical samples due to formation of volatile germanium compounds during the course of dissolution. It has been stated that this is especially true for the samples having high sulfide and/or chloride content.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 6
    Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues
    (Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen
    A multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.