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Record W4293104734 · doi:10.3390/physics4020025

Ion Beam Modification for Si Photonics

2022· article· en· W4293104734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotonicsMaterials scienceOptoelectronicsFabricationIon implantationSiliconGermaniumSilicon photonicsQuantum dotIon beamDetectorIonNanotechnologyOpticsBeam (structure)ChemistryPhysics

Abstract

fetched live from OpenAlex

Ion implantation has played a significant role in semiconductor device fabrication and is growing in significance in the fabrication of Si photonic devices. In this paper, recent progress in the growth and characterization of Si and Ge quantum dots (QDs) for photonic light-emitting devices is reviewed, with a focus on ion implantation as a synthetic tool. Light emissions from Si and Ge QDs are compared with emissions from other optically active centers, such as defects in silicon oxide and other thin film materials, as well as rare-earth light emitters. Detection of light in silicon photonics is performed via the integration of germanium and other elements into detector structures, which can also be achieved by ion implantation. Novel techniques to grow SiGe- and SiGeSn-on-Si structure are described along with their application as detectors for operation in the short-wave infrared range.

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.

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 score0.022
Threshold uncertainty score0.278

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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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