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Record W2523806856 · doi:10.1149/ma2016-02/17/1506

In-Situ Rare Earth Doping of Silicon-Based Nanostructures By Plasma Enhanced Chemical Vapour Deposition

2016· article· en· W2523806856 on OpenAlexaff
Peter Mascher, Jacek Wójcik, Zahra Khatami, Jeremy W. Miller

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceDopantPlasma-enhanced chemical vapor depositionElectron cyclotron resonanceDopingChemical vapor depositionSputter depositionSiliconNanotechnologySputteringPlasmaOptoelectronicsDeposition (geology)Thin filmPhysicsGeology

Abstract

fetched live from OpenAlex

In order for Si-based materials to be used in solid-state lighting (SSL) schemes it is necessary to have precise control of the optical emission from these materials. This can be accomplished through the use of rare earth dopants such as Ce, Tb, and Eu to obtain blue, green, and red emissions, respectively. After a brief review of the latest developments in the field, this talk will focus on several in-situ doping approaches to achieving very high, optically active concentrations of the rare earths. The methodologies include electron cyclotron resonance plasma enhanced chemical vapour deposition (ECR-PECVD), inductively coupled plasma (ICP) CVD as low thermal budget processes for film deposition, reactive sputtering, as well as the use of a recently installed Circular High Vacuum Magnetron Sputtering source attached to the ECR-PECVD tool. We will describe the salient features of the deposition systems and correlate important process parameters with the observed luminescence. Finally, we will discuss some of the challenges in developing electrically driven lighting cells suitable for SSL and in particular, for the development of widely tuneable Si-based light sources. This work has been supported by the Natural Sciences and Engineering Research Council (NSERC) under its Discovery Grants Program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 teacher head, 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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