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Record W2004161845 · doi:10.1149/06410.0115ecst

The Role of Passivation Layer during Thermal Annealing for Oxide Semiconductor Thin Films

2014· article· en· W2004161845 on OpenAlexaff
Chi‐Sun Hwang, Sang‐Hee Ko Park, Sung-Heang Cho, Min Ki Ryu, Himchan Oh, Su‐Jae Lee, Jong‐Heon Yang, Chun‐Won Byun, Jonghyurk Park, Kyung Ik Cho, Hye Yong Chu

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsKootenay Association for Science & Technology
FundersMinistry of Science, ICT and Future Planning
KeywordsPassivationMaterials scienceAnnealing (glass)DopantPlasma-enhanced chemical vapor depositionOxideOptoelectronicsThin filmLayer (electronics)Oxide thin-film transistorSheet resistanceSputteringDopingThin-film transistorSemiconductorComposite materialNanotechnologySiliconMetallurgy

Abstract

fetched live from OpenAlex

The change of the resistance of active films during thermal annealing was monitored under the split of passivation layers and active layers. ALD deposited Al 2 O 3 layer and PECVD deposited SiO 2 layer were applied for the passivation layer and sputter deposited Al-doped InZnSnO and InGaZnO were applied for the active layer. It is found that Al 2 O 3 passivation layer supplies dopants to the active interface during high temperature annealing and the side wall of Al:IZTO pattern contains many defects, which become conductive during high temperature annealing through detailed measurement of resistance of patterned oxide semiconductor thin films with various sizes. It is suggested that PECVD deposited SiO 2 layer with proper pre-treatment (possibly N 2 O plasma treatment) will be best candidate for the passivation layer in the oxide TFTs, especially in the case of oxide TFTs with high mobility.

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 score0.019
Threshold uncertainty score0.286

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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