MétaCan
Menu
Back to cohort
Record W1992621504 · doi:10.1063/1.2472601

Online system for temperature and accumulated dose control in plasma-based ion implantation

2007· article· en· W1992621504 on OpenAlexafffund
F. Roy, G. Abel, B. Terreault, A. Reguer, Jean‐Luc Meunier, M. Bolduc, J.R.H. Ross

Bibliographic record

VenueReview of Scientific Instruments · 2007
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluenceMaterials scienceIon implantationIonPlasmaDeconvolutionAtomic physicsAnalytical Chemistry (journal)OpticsChemistryPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Surface treatment optimization requires the control of the ion dose and the workpiece temperature, two parameters that are not trivially measurable in plasma-based ion implantation. A temperature and ion fluence monitoring system has been developed and implemented in a plasma-based ion implanter. It is based on the measurement with a thermopile of the radiation emitted from the back face of a thin copper disk inserted in the stainless steel sample holder. Since the incident ions carry practically all the incident power, the measurement of the Cu disk temperature that increases during implantation can provide an evaluation of the ion fluence in real time. A model has been developed for the deconvolution of the temperature data and has been fitted to the temperature behavior during implantation. A good agreement between the total integrated doses, evaluated with Rutherford backscattering spectroscopy characterization, and the ion fluence calculated by means of this model has been obtained with a discrepancy less than 16%.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueReview of Scientific InstrumentsSame topicMetal and Thin Film MechanicsFrench-language works237,207