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Record W2786660273 · doi:10.1063/1.5000362

Effect of dual-dielectric hydrogen-diffusion barrier layers on the performance of low-temperature processed transparent InGaZnO thin-film transistors

2018· article· en· W2786660273 on OpenAlexafffund
Alireza Tari, William S. Wong

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePlasma-enhanced chemical vapor depositionThin-film transistorPassivationGate dielectricOptoelectronicsDielectricThin filmChemical vapor depositionLayer (electronics)NanotechnologyTransistorElectrical engineering

Abstract

fetched live from OpenAlex

Dual-dielectric SiOx/SiNx thin-film layers were used as back-channel and gate-dielectric barrier layers for bottom-gate InGaZnO (IGZO) thin-film transistors (TFTs). The concentration profiles of hydrogen, indium, gallium, and zinc oxide were analyzed using secondary-ion mass spectroscopy characterization. By implementing an effective H-diffusion barrier, the hydrogen concentration and the creation of H-induced oxygen deficiency (H-Vo complex) defects during the processing of passivated flexible IGZO TFTs were minimized. A bilayer back-channel passivation layer, consisting of electron-beam deposited SiOx on plasma-enhanced chemical vapor-deposition (PECVD) SiNx films, effectively protected the TFT active region from plasma damage and minimized changes in the chemical composition of the semiconductor layer. A dual-dielectric PECVD SiOx/PECVD SiNx gate-dielectric, using SiOx as a barrier layer, also effectively prevented out-diffusion of hydrogen atoms from the PECVD SiNx-gate dielectric to the IGZO channel layer during the device fabrication.

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 categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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.

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

Citations15
Published2018
Admission routes2
Has abstractyes

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