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Record W3024035604 · doi:10.1149/ma2020-01161071mtgabs

(Invited) Characterization of Novel Nanostructured Terbium Doped Oxygen Rich Silicon Oxide for Photonic Applications

2020· article· en· W3024035604 on OpenAlexaff
Zahra Khatami, Peter Mascher

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsMcMaster UniversityUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceSiliconDopantPhotoluminescenceNanocrystalline siliconSilicon oxideAnnealing (glass)DopingNanotechnologyAnalytical Chemistry (journal)Chemical engineeringOptoelectronicsAmorphous siliconCrystalline siliconSilicon nitrideMetallurgyChemistry

Abstract

fetched live from OpenAlex

Rare earth doped silicon-based thin films can be used for light emitting applications, where the control of dopant type and concentration plays a significant role in the enhancement of optical functionalities [1]. In this work, nanostructured terbium (Tb) doped oxygen-rich silicon oxide (ORSO) samples were fabricated using a novel fabrication technique: integrated sputtering and plasma enhanced chemical vapor deposition (PECVD) [2]. The Tb dopant concentration in the ORSO host matrix was varied from 0.5 at. % to 17 at. % and post-deposition thermal annealing was performed in a wide range of temperatures in a N2 atmosphere. In addition, the presence of defects, one of the general physical problems limiting the emission efficiency of rare earth doped silicon-based materials, was investigated using a combination of photoluminescence measurements and positron annihilation spectroscopy (PAS). We discuss the influence of the Tb dopant concentration on the silicon nanocrystal growth and the formation of Tb silicate nanocrystals. A precipitation mechanism as a function Tb content and annealing temperature is proposed and tested using X-ray diffraction, photoluminescence, and microscopy techniques. Following post-deposition annealing at 1200 °C, all Tb ions agglomerate and form large nanocrystals with diameters reaching 50 nm (Fig. 1). Scanning transmission electron microscopy (STEM) analysis shows that these large nanocrystals are composed of Tb, silicon, and oxygen, however, no Tb remains in the amorphous regions of the silicon oxide matrix. Despite the oxygen-rich content, small silicon nanocrystals (Si-ncs) are formed in the matrix with a size distribution between 2 and 5 nm. The influence of hydrogen passivation and the contribution of trap states to the luminescence mechanism is discussed using the interdependency of the photoluminescence emission for the dominant Tb excited state (5D4 to 7F5) and the values of the S-parameter obtained from PAS. The values of the S- and W-parameters identify the traps at the interface between the amorphous silicon oxide host matrix and embedded nanocrystals. Finally, the solubility level of Tb ions is extended using this novel processing method and an optimized Tb concentration for the most luminescent matrix is suggested. High-temperature annealing promoted the formation of a different Tb silicate crystal phase than the phases observed in samples with lower Tb content. A fiber texture with no preferred orientation in the plane but growth direction of <200> is observed [Fig. 2]; however, samples with lower Tb content are fully random and show no distinct texture. The correlation between the nanostructure and emission properties is an important step to fabricating Tb-doped silicon oxide materials which can be potentially used in a variety of light emitting applications such as displays, solar cells, optical communications, and other silicon optoelectronic devices. Fig. 1 High-resolution transmission electron microscopy (HR-TEM) image of a focused ion beam (FIB) prepared Tb-doped ORSO thin film annealed at 1200 ℃ for 1 hour in N2 atmosphere. Fig. 2 A fiber texture is evident by the ring of intensity shown in the pole figure and no preferred orientation in plane is observed. References: Li, O. Zalloum, T. Roschuk, C. Heng, J. Wojcik, P. Mascher “The formation of light emitting cerium silicates in cerium-doped silicon oxides”, Appl. Phys. Lett. 94, 011112 (2009). W. Miller, Z. Khatami, J. Wojcik, J. D. B. Bradley, P. Mascher “Integrated ECR-PECVD and magnetron sputtering system for rare-earth-doped Si-based materials”, Surf. Coat. Techn., 336, 99-105 (2018). Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.242
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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