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Record W2285940259 · doi:10.1149/06601.0233ecst

Optically Transparent Flexible IGZO TFTs Fabricated with a Selective Wet-Etch Process

2015· article· en· W2285940259 on OpenAlexaff
Alireza Tari, Czang-Ho Lee, William S. Wong

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceThin-film transistorFlexible displayOptoelectronicsFabricationThreshold voltageTransistorFlexible electronicsLift (data mining)Etching (microfabrication)VoltageNanotechnologyElectrical engineeringComputer scienceLayer (electronics)

Abstract

fetched live from OpenAlex

Given the relatively poor selectivity to common semiconductor etchants, IGZO (InGaZnO) thin film transistors (TFTs) are typically fabricated using lift-off processes to define the electrical contacts. However, the success of the emerging transition-metal oxide materials system for flexible electronics requires compatibility with conventional TFT fabrication processes. In this research, flexible IGZO TFTs having 85% optical transparency in the visible regime were fabricated on polyethylene napthalate (PEN) substrates using a selective wet etching process to eliminate the need for lift-off processing. The devices were processed directly onto the plastic platform at the maximum temperature of 150°C. The fabricated TFTs exhibited a field-effect mobility of ~12.5 cm2/V.sec, threshold voltage of ~5.0 V, and an Ion/Ioff ratio of <106. No current crowding behavior was observed for the TFTs at the low drain-source voltage (VD) regime. The highly selective etching process provides a means to fabricate IGZO based circuits with low processing complexity that will enable system-on-“plastic” integration of flexible transparent flexible displays.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.030
GPT teacher head0.241
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2015
Admission routes1
Has abstractyes

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