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Record W1996554666 · doi:10.1063/1.1803615

Raman study of As outgassing and damage induced by ion implantation in Zn-doped GaAs

2004· article· en· W1996554666 on OpenAlexaff
David Barba, Vincent Aimez, Jacques Beauvais, J. Beerens, Dominique Drouin, M. Chicoine, F. Schiettekatte

Bibliographic record

VenueJournal of Applied Physics · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor materials and interfaces
Canadian institutionsUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsRaman spectroscopyMaterials scienceIon implantationDopingHeterojunctionIonThermal desorptionPhononAnalytical Chemistry (journal)Molecular physicsOptoelectronicsDesorptionCondensed matter physicsChemistryOpticsAdsorption

Abstract

fetched live from OpenAlex

Room temperature micro-Raman investigations of LO phonon and LO phonon-plasmon coupling is used to study the As outgassing mechanism and the disordering effects induced by ion implantation in Zn-doped GaAs with nominal doping level p=7×1018cm−3. The relative intensity of these two peaks is measured right after rapid vacuum thermal annealings (RVTA) between 200 and 450°C, or after ion implantations carried out at energies of 40keV with P+, and at 90 and 170keV with As+. These intensities provide information regarding the Schottky barrier formation near the sample surface. Namely, the Raman signature of the depletion layer formation resulting from As desorption is clearly observed in samples submitted to RVTA above 300°C, and the depletion layer depths measured in ion implanted GaAs:Zn are consistent with the damage profiles obtained through Monte Carlo simulations. Ion channeling effects, maximized for a tilt angle set to 45° during implantation, are also investigated. These results show that the Raman spectroscopy is a versatile tool to study the defects induced by postgrowth processes in multilayered heterostructures, with probing range of about 100nm in GaAs-based materials.

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 score0.020
Threshold uncertainty score0.360

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 head0.018
GPT teacher head0.275
Teacher spread0.257 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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