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Record W2889685640 · doi:10.1063/1.5044456

Surface reaction kinetics in atomic layer deposition: An analytical model and experiments

2018· article· en· W2889685640 on OpenAlex
Triratna Muneshwar, Ken Cadien

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of Alberta
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsAtomic layer depositionChemistryChemisorptionSubstrate (aquarium)PhysisorptionDesorptionKineticsDeposition (geology)Chemical kineticsReaction rate constantChemical engineeringAdsorptionThermodynamicsPhysical chemistryAnalytical Chemistry (journal)Layer (electronics)ChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Atomic layer deposition (ALD) surface reactions are comprised of several elementary surface interactions (such as physisorption, desorption, and chemisorption) occurring at the substrate. Since ALD processes are often far from thermodynamic equilibrium, the surface saturation behavior is controlled by the kinetics of these involved interactions. In this article, we present a first-order kinetic model for ALD reaction, to simulate the cumulative effect of precursor exposure (tA), post-precursor purge (tP1), reactant exposure (tB), post-reactant purge (tP2), and substrate temperature (Tsub) on the resulting growth per cycle (GPC) in an ABAB… pulsed ALD process. Furthermore, to simulate the effect of inadequate reactor purges (tP1, and/or tP2) and undesired non-ALD side reactions, reaction pathways to account excess GPC are also taken into consideration. From our model calculations, we simulate GPC vs Tsub trends observed in ALD growth experiments and demonstrate that the process temperature window (ΔTALD) for a constant GPC depends upon the deposition cycle parameters tA, tP1, tB, and tP2. The modeled GPC vs Tsub trends are discussed and compared with SiNx, ZrN, and ZnO PEALD growth experiments.

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 score0.397
Threshold uncertainty score0.280

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 head0.033
GPT teacher head0.280
Teacher spread0.246 · 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