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Record W2024169750 · doi:10.1143/apex.4.021302

Linewidth of Low-Field Electrically Detected Magnetic Resonance of Phosphorus in Isotopically Controlled Silicon

2011· article· en· W2024169750 on OpenAlexaff
Hiroki Morishita, Eisuke Abe, Waseem Akhtar, L. S. Vlasenko, Akira Fujimoto, Kentarou Sawano, Yasuhiro Shiraki, L. Dreher, H. Riemann, Nikolai V. Abrosimov, Peter Becker, Hans-J. Pohl, M. L. W. Thewalt, Martin S. Brandt, Kohei M. Itoh

Bibliographic record

VenueApplied Physics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsSimon Fraser University
FundersEngineering and Physical Sciences Research Council
KeywordsElectron paramagnetic resonanceLaser linewidthSpinsPulsed EPRParamagnetismSiliconElectronRecombinationElectron nuclear double resonancePhosphorusChemistrySpin (aerodynamics)Resonance (particle physics)SemiconductorMagnetic fieldAtomic physicsMaterials scienceNuclear magnetic resonanceCondensed matter physicsSpin echoOptoelectronicsMagnetic resonance imagingPhysicsOptics

Abstract

fetched live from OpenAlex

The linewidth of the low-field electrically detected magnetic resonance (LFEDMR) of phosphorus electrons in silicon is investigated using samples with various 29 Si nuclear spin fractions and is compared to that of X-band electron paramagnetic resonance (EPR). The linewidths of LFEDMR and EPR are the same even though LFEDMR signals are obtained based on spin-dependent recombination, suggesting that the interaction between electron spins of phosphorus and recombination centers is strong enough for the LFEDMR detection but weak enough not to affect the linewidths. This favorable balance makes LFEDMR an attractive method to elucidate the low-field behavior of paramagnetic defects in semiconductors.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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