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Record W2120551123 · doi:10.1109/ted.2006.881012

Equivalent Circuit Description of Threshold Voltage Shift in a-Si:H TFTs From a Probabilistic Analysis of Carrier Population Dynamics

2006· article· en· W2120551123 on OpenAlexaff
Sanjiv Sambandan, Arokia Nathan

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2006
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThin-film transistorThreshold voltageProbabilistic logicAmorphous siliconPopulationOptoelectronicsTransistorBiasingElectronic circuitMaterials scienceLogic gateElectrical engineeringElectronic engineeringComputer scienceVoltageEngineeringSiliconNanotechnologyArtificial intelligence

Abstract

fetched live from OpenAlex

Amorphous hydrogenated silicon thin-film transistors (TFTs) are critical components in large area display and sensor systems, and the need for TFT circuits has been increasing. However, the intrinsic metastability associated with the TFT leads to a threshold voltage shift (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> shift) with time, under prolonged gate bias. For design of reliable TFT circuits, it is imperative to accurately predict this instability for time-varying analog gate bias. In this paper, the author model the threshold voltage variation using a probabilistic analysis of the electron population dynamics as prescribed by the defect pool and charge trapping mechanisms. The model is then extended for prediction of the effect of variable gate bias, and in particular the device history, on the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> shift. Based on this model, a passive equivalent circuit is synthesized to accurately predict the V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> shift in TFTs for applications in active matrix organic light emitting diode displays and sensors

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 score0.447
Threshold uncertainty score0.989

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

Explore more

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