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Record W2042249506 · doi:10.1063/1.1927275

General model and segregation coefficient measurement for ultrashallow doping by excimer laser annealing

2005· article· en· W2042249506 on OpenAlexaff
Jean-Numa Gillet, Jean-Yves Degorce, Michel Meunier

Bibliographic record

VenueApplied Physics Letters · 2005
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDopantExcimer laserMaterials scienceDopingAnnealing (glass)LaserDiffusionAnalytical Chemistry (journal)ThermodynamicsChemistryOpticsOptoelectronicsMetallurgyPhysics

Abstract

fetched live from OpenAlex

A general model of ultrashallow doping by excimer laser annealing is derived from only one diffusion-segregation equation. In our model, the relative dopant profile after some laser shots reaches a stationary distribution, which only depends on the segregation and liquid-phase diffusion coefficients of the dopant but not on the laser-process parameters. From this result, a one-point method is proposed to experimentally determine the out-of-equilibrium segregation coefficient k. Only the relative dopant concentration at the material surface has to be measured prior to determine the k value. Experimental dopant profiles are compared to simulations generated with experimental k values.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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

Citations14
Published2005
Admission routes1
Has abstractyes

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