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Record W2107985063 · doi:10.1109/ted.2003.818156

Above-threshold parameter extraction and modeling for amorphous silicon thin-film transistors

2003· article· en· W2107985063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2003
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThin-film transistorAmorphous siliconMaterials scienceThreshold voltageOptoelectronicsTransistorExtraction (chemistry)Saturation (graph theory)Amorphous solidFabricationSiliconElectron mobilityBand gapContact resistanceOxide thin-film transistorElectronic engineeringVoltageLayer (electronics)Crystalline siliconElectrical engineeringNanotechnologyEngineering

Abstract

fetched live from OpenAlex

This paper presents modeling and parameter extraction of the above-threshold characteristics of hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) in both linear and saturation regions of operation. A bias- and geometry-independent definition for field effect mobility considering the ratio of free-to-trapped carriers is introduced, which conveys the properties of the active semiconducting layer. A method for extraction of model parameters such as threshold voltage, effective mobility, band-tail slope, and contact resistance from the measurement results is presented. This not only provides insight to the device properties, which are highly fabrication-dependent, but also enables accurate and reliable TFT circuit simulation. The techniques presented here form the basis for extraction of physical parameters for other TFTs with similar gap properties, such as organic and polymer TFTs.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
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
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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