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Record W1970156869 · doi:10.1116/1.1428277

Inductively coupled plasma etching of InP using CH4/H2 and CH4/H2/N2

2002· article· en· W1970156869 on OpenAlexafffund
Hsin‐Yi Chen, Harry E. Ruda

Bibliographic record

VenueJournal of Vacuum Science & Technology B Microelectronics and Nanometer Structures Processing Measurement and Phenomena · 2002
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEtching (microfabrication)X-ray photoelectron spectroscopyInductively coupled plasmaAnalytical Chemistry (journal)PlasmaSurface roughnessChemistryHydrogenReactive-ion etchingMaterials scienceLayer (electronics)NanotechnologyChemical engineeringComposite materialEnvironmental chemistry

Abstract

fetched live from OpenAlex

Inductively coupled plasma etching of InP in CH4/H2 and CH4/H2/N2 gas mixtures was studied to understand the etching mechanisms and the influence of etching gas composition on etching rate, etching profile, and surface morphology. CH4/H2 plasmas generally had higher etching rates than CH4/H2/N2 plasmas. Deterioration of InP surfaces, following etching, reflected the preferential loss of P over In due to the diffusivity and reactivity of H being higher than CH3 on InP surfaces, and also since PH3 is more volatile than In(CH3)3. In extreme circumstances, this can lead to the formation of In-rich droplets on the surface, with associated surface roughening. This was supported by the opposing trends of surface roughness (measured using atomic force microscopy) and P/In ratio (from x-ray photoelectron spectroscopy) as a function of the CH4 gas concentration for CH4/H2 gas mixtures. The addition of N2 to the CH4/H2 plasmas improved the surface morphology as N radicals reduced the rate of P removal by reacting with H radicals. However, an inevitable increase in the N+ and N2+ concentrations led to erosion of the SiO2 masks and caused sloping sidewalls.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.218
Teacher spread0.192 · 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

Citations10
Published2002
Admission routes2
Has abstractyes

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