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Record W759364117 · doi:10.1116/1.4926382

Influence of atomic layer deposition valve temperature on ZrN plasma enhanced atomic layer deposition growth

2015· article· en· W759364117 on OpenAlexafffund
Triratna Muneshwar, Ken Cadien

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2015
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsAtomic layer depositionEvaporationMaterials scienceElectrical resistivity and conductivityDeposition (geology)Layer (electronics)Thin filmBoiling pointCondensationPlasmaNanotechnologyChemistryOrganic chemistryThermodynamicsElectrical engineering

Abstract

fetched live from OpenAlex

Atomic layer deposition (ALD) relies on a sequence of self-limiting surface reactions for thin film growth. The effect of non-ALD side reactions, from insufficient purging between pulses and from precursor self-decomposition, on film growth is well known. In this article, precursor condensation within an ALD valve is described, and the effect of the continuous precursor source from condensate evaporation on ALD growth is discussed. The influence of the ALD valve temperature on growth and electrical resistivity of ZrN plasma enhanced ALD (PEALD) films is reported. Increasing ALD valve temperature from 75 to 95 °C, with other process parameters being identical, decreased both the growth per cycle and electrical resistivity (ρ) of ZrN PEALD films from 0.10 to 0.07 nm/cycle and from 560 to 350 μΩ cm, respectively. Our results show that the non-ALD growth resulting from condensate accumulation is eliminated at valve temperatures close to the pressure corrected boiling point of precursor.

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 score0.006
Threshold uncertainty score0.866

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.233
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

Citations12
Published2015
Admission routes2
Has abstractyes

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