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Record W2324784691 · doi:10.1149/1.3485682

The Impact of Helium Co-Implantation on Hydrogen Induced Donor Profiles in Float Zone Silicon

2010· article· en· W2324784691 on OpenAlexaff
Johannes G. Laven, R. Job, Hans‐Joachim Schulze, F.‐J. Niedernostheide, Volker Häublein, Holger Schulze, Werner Schustereder, H. Ryssel, L. Frey

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsAnnealing (glass)FluenceIrradiationHydrogenHeliumWaferProtonMaterials scienceDopingSiliconRadiation damageAnalytical Chemistry (journal)Ion implantationRadiochemistryAtomic physicsChemistryOptoelectronicsIonComposite materialNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Doping profiles in proton and helium co-implanted and annealed n-type float zone silicon wafers are analyzed by means of spreading resistance measurements. After annealing at sufficiently high temperatures and/or long-enough duration, the hydrogen-related donor profiles known from proton implantations are significantly enhanced by the helium irradiation. The resulting profile shape exhibits a strong resemblance to the radiation damage distribution of the co-implantation. By increasing the ultimately introduced num-ber of hydrogen related donors without varying the available amount of hydrogen, it is shown that the donor profile resulting from proton implantation and annealing is limited by the defect species and the implanted hydrogen is supplied in surplus. The maximum of the additionally induced donor distribution by the he-lium implantation shows a different dependency on the fluence compared to the donor maximum induced by mere proton irradia-tion. The diffusion of protons through the irradiated layer during annealing is impacted by the He-irradiation.

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 score0.022
Threshold uncertainty score0.308

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 head0.013
GPT teacher head0.257
Teacher spread0.245 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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