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Record W2080476129 · doi:10.1116/1.1484095

Thin film transistors with anodic gate dielectrics and chemical bath deposited active layers

2002· article· en· W2080476129 on OpenAlexaff
Feng Yuan Gan, I. Shih

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceThin-film transistorDielectricOptoelectronicsGate dielectricThreshold voltageAnodeCapacitorField effectLeakage (economics)TransistorLayer (electronics)VoltageElectrical engineeringElectrodeNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Thin film transistors (TFTs) with an anodic dielectric layer and chemical bath deposited active layer have been fabricated on glass substrates. The effects of anodization conditions on the quality of the anodic dielectric were investigated by measuring the breakdown electric field and leakage current density of the metal–insulator–metal capacitors fabricated. Using the anodic gate dielectric, preliminary thin film transistors with CdS and CdSe films formed by a chemical bath deposition method were fabricated and evaluated. Results of electrical measurements carried out on the CdSe-TFTs with Al2O3 and Ta2O5 gate dielectrics showed field effect mobilities of 3.4 and 0.67 cm2/V s and threshold voltages of 3.2 and 8.2 V, respectively. Field effect mobilities of 0.2 and 2 cm2/V s and threshold voltages of 4.3 and 5.2 V were observed for CdS-TFTs, again with Al2O3 and Ta2O5 gate dielectrics. The mobilities obtained from the present TFTs fabricated on glass substrates are smaller than that on similar TFTs fabricated on thermally grown gate oxides on Si substrates in our previous work.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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
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
Published2002
Admission routes1
Has abstractyes

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