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Record W2160456201 · doi:10.1109/led.2005.848116

Markov model for threshold-voltage shift in amorphous silicon TFTs for variable gate bias

2005· article· en· W2160456201 on OpenAlexaff
Sanjiv Sambandan, Lei Zhu, Denis Striakhilev, Peyman Servati, Arokia Nathan

Bibliographic record

VenueIEEE Electron Device Letters · 2005
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThreshold voltageMaterials scienceOptoelectronicsSiliconAmorphous siliconLogic gateAmorphous semiconductorsVariable (mathematics)Amorphous solidVoltageElectronic engineeringElectrical engineeringComputer scienceTransistorEngineeringChemistryMathematicsCrystalline siliconCrystallography

Abstract

fetched live from OpenAlex

Amorphous silicon (a-Si:H) thin-film transistors (TFTs) are widely used in active matrix displays and sensors, in which their operation is typically analog in nature. However, the TFT experiences a V/sub T/ shift with time under gate bias, and the need for a model of the V/sub T/ shift with variable gate bias is imperative for robust circuit design. A model for the V/sub T/ shift under constant and variable gate bias has been presented and agrees with measurement results. The developed model can be easily represented by circuit elements and incorporated into a circuit simulator. As a proof of concept, we use the model to predict the transients of a weighted voltage subtractor circuit.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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 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

Citations32
Published2005
Admission routes1
Has abstractyes

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