Bibliographic record
Abstract
This paper investigates the reverse current-voltage (I-V) characteristics of inverted staggered hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs). Three mechanisms have been identified as the source of the reverse current: ohmic conduction, front channel conduction, and backchannel conduction. Ohmic conduction constitutes the physical limit for the reverse current and is due to the intrinsic conductivity of the a-Si:H and associated dielectric layers, which correlates with process integrity. The accumulation of holes and electrons at the front and back a-Si:H/a-SiN/sub x/:H interfaces, respectively, forms the basis of the other two leakage mechanisms. The relative dominance of the one or the other mechanism depends on bias conditions, TFT geometry, and process conditions. This paper identifies these sources of leakage current and examines the effect of the critical geometrical parameters (such as channel length and overlap length) and bias conditions on these leakage components. Physical models to predict bias and geometry dependences are presented for a quantitative analysis of the leakage current. Modeling results corroborate experimental observations of leakage current extracted from a large number of TFTs that are put in parallel for improved measurement accuracy. The physical parameters of the model provide a method for estimation of the significant interface and bulk properties of the structure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".