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Record W2093141452 · doi:10.1149/1.2721476

Optimization of Plasma Enhanced Atomic Layer Deposition Processes for Oxides, Nitrides and Metals in the Oxford Instruments FlexAL Reactor

2007· article· en· W2093141452 on OpenAlexaff
Chris Hodson, Nick Singh, S. B. S. Heil, Hans van Hemmen, Erwin Kessels

Bibliographic record

VenueECS Transactions · 2007
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsOxford Instruments (Canada)
FundersHanyang University
KeywordsAnalytical Chemistry (journal)Materials sciencePlasmaElectrical resistivity and conductivityNitrideStoichiometryTinWaferNitrogenAtomic layer depositionTitanium nitrideSaturation (graph theory)TitaniumSiliconThin filmLayer (electronics)ChemistryMetallurgyNanotechnologyEnvironmental chemistry

Abstract

fetched live from OpenAlex

Hafnium oxide films deposited on silicon wafers from TEMAH and O2 plasma showed saturation at growth rate per cycle of 1.1Aå, which was independent of the plasma conditions. The same film deposited thermally using H2O as the oxidant saturated at 0.8Aå/cycle. By varying the plasma exposure time the compositional ratio of [O]/[Hf], as calculated from RBS measurements, changed from 2.0 to 2.13. The carbon content in plasma HfO2 films was < 2% compared to 8% in thermal HfO2 films. Titanium nitride films deposited on silicon wafers from TiCl4 and N2 / H2 plasma showed saturation at 0.33Aå/cycle, which was independent of plasma conditions and a resistivity of <170µΩ cm at 350{degree sign}C deposition temperature. The stoichiometry of the films can be changed from being slightly nitrogen rich to titanium rich by varying the N:H ratios in the plasma and limiting the amount of nitrogen available for the reaction. The chlorine impurity in TiN varied according to plasma exposure time (2.6% to 1.2%) and N2:H2 gas ratio in the plasma, with a corresponding change in resistivity (200µΩ - 150µΩ).

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 score0.133
Threshold uncertainty score0.261

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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