MétaCan
Menu
Back to cohort

Conversion Efficiency of Radiation Damage Profiles into Hydrogen-Related Donor Profiles

2011· article· en· W2067816626 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.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsHydrogenMaterials scienceRadiation damageHeliumAnnealing (glass)SiliconDopingIrradiationAnalytical Chemistry (journal)Atomic physicsRadiochemistryChemistryOptoelectronicsComposite materialNuclear physics

Abstract

fetched live from OpenAlex

By introducing radiation damage and hydrogen and successively annealing with low thermal budgets, hydrogen-related donors are created in oxygen-lean silicon. Hydrogen-related donor profiles are induced in float-zone silicon by implanting hydrogen and/or helium and successive annealing with or without additional hydrogen introduction by a hydrogen plasma. The efficiency of the conversion of the radiation-induced damage into the hydrogen-related donors differs in dependence of the method of damage and hydrogen introduction. In proton implanted samples, the ultimate introduction rate of the donors is significantly lower than it is in helium and hydrogen co-implanted samples. Furthermore, the depth distribution of the hydrogen-related donors shows a deviance from the simulated distribution of the radiation damage induced by proton implantation not seen in case of helium-induced damage. The change in doping efficiency is discussed in respect to the hydrogen content in the different experiments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0030.003
Research integrity0.0000.001
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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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