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Record W2947201552 · doi:10.1103/physrevb.99.195207

Shallow donor complexes formed by pairing of double-donor magnesium with group-III acceptors in silicon

2019· article· en· W2947201552 on OpenAlexafffund
R. J. Abraham, V. B. Shuman, L. М. Portsel, А. N. Lodygin, Yu. A. Astrov, N. V. Abrosimov, S. G. Pavlov, Heinz‐Wilhelm Hübers, Stephanie Simmons, M. L. W. Thewalt

Bibliographic record

VenuePhysical review. B./Physical review. B · 2019
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaRussian Foundation for Basic ResearchDeutsche Forschungsgemeinschaft
KeywordsMagnesiumShallow donorExcitonExcited stateAcceptorIonization energyBoronIonizationPairingAtomic physicsSpectral lineBinding energyAbsorption spectroscopySiliconPhotoluminescenceImpurityMaterials scienceChemistryPhysicsIonCondensed matter physics

Abstract

fetched live from OpenAlex

Magnesium in silicon primarily occupies an interstitial site, where it acts as a moderately deep double donor. It has recently been shown that interstitial magnesium can pair with the substitutional acceptor boron to form a shallow single-donor center. In this work, we demonstrate analogous complexing with the other group-III acceptors Ga, In, and Al. We observe the odd-parity excited states of each shallow donor complex in absorption spectra, from which the ionization energies are obtained. These complexes can localize excitons, and we observe the donor bound exciton transitions of all four centers in photoluminescence spectra. The Mg-acceptor complexes are found to obey Haynes rule, which predicts a linear relationship between donor ionization energy and donor bound exciton localization energy.

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 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 score0.004

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 communication0.0010.000
Open science0.0000.001
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 head0.016
GPT teacher head0.335
Teacher spread0.318 · 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".

Quick stats

Citations11
Published2019
Admission routes2
Has abstractyes

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