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Record W2810086026 · doi:10.1109/ted.2018.2847612

Analysis of the Channel and Contact Regions in Staggered and Drain-Offset ZnO Thin-Film Transistors With Compact Modeling

2018· article· en· W2810086026 on OpenAlexafffund
Michael Alex, Douglas W. Barlage

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2018
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Alberta
FundersScience and Engineering Research CouncilAlberta InnovatesCMC Microsystems
KeywordsThin-film transistorMaterials scienceTransistorOptoelectronicsOffset (computer science)ElectrodeChannel (broadcasting)Oxide thin-film transistorThreshold voltageElectrical engineeringVoltageLayer (electronics)NanotechnologyComputer scienceEngineeringChemistry

Abstract

fetched live from OpenAlex

Zinc oxide thin-film transistors (TFTs) with different device geometries and source/drain (S/D) contact metallizations are investigated. To facilitate the analysis, the channel and contact regions in top-gated staggered and drain-offset TFTs employing various permutations of ruthenium (Ru) and gold (Au) bottom electrodes are quantified with a TFT device model. The staggered TFT structure employs identical geometric overlaps between the gate-and-S/D electrodes; whereas the drain-offset TFT structure replaces the gate-to-drain overlap with a geometric gap in the channel. First, the experimental TFT current-voltage (I-V) characteristics are disentangled into the intrinsic channel and contact components by the transmission line method. The contact and channel behaviors are then individually modeled using a universal transistor compact model. From evaluating the model parameters, the diverse device characteristics observed in this paper are attributed to the Au electrode's electron doping effect altering the properties of both the TFT's contact and channel regions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations8
Published2018
Admission routes2
Has abstractyes

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