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Record W1996563446 · doi:10.1063/1.2349544

Influence of ion mixing on the energy dependence of the ion-assisted chemical etch rate in reactive plasmas

2006· article· en· W1996563446 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

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsIonChemistryPlasmaWork (physics)Reactive-ion etchingYield (engineering)ChlorinePlasma etchingDesorptionReaction rateAnalytical Chemistry (journal)Etching (microfabrication)Materials scienceThermodynamicsPhysical chemistryAdsorptionChromatography

Abstract

fetched live from OpenAlex

Recently, Stafford et al. [Appl. Phys. Lett. 87, 071502 (2005)] have shown that in contrast to the etch yield on a saturated surface, the ion-assisted chemical etch rate cannot universally be modeled by a simple square-root energy dependence. This results from the surface coverage by reactive neutral species being also a function of the ion energy. In this work, we further point out that depending on the plasma-material combination, the etch rate can exhibit two regimes that are characterized by different dependences on the ion energy. While these results are inconsistent with currently available models, we show that they can be interpreted by taking into account ion mixing effects on the desorption rate of volatile reaction products involved in the model of Stafford et al. Application of this rate model to the etching of Si, SiO2, HfO2, and ZrO2 in chlorine and fluorine plasma chemistries provides an excellent description of the simultaneous dependence of the etch rate on ion energy and on ion and reactive neutral fluxes.

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)0.000
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 score0.475
Threshold uncertainty score0.221

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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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