Resolving the Unusual Gate Leakage Currents of Thin-Film Transistors with Single-Walled Carbon-Nanotube-Based Active Layers
Bibliographic record
Abstract
Solution-processed single-walled carbon nanotube (SWCNT) thin-film transistors (TFTs) in the research stage often have large active areas. This results in unusual gate leakage currents with high magnitudes that vary with applied voltages. In this paper, we report an improved structure for solution-processed SWCNT-based TFTs. The unusual gate leakage current in the improved structure is resolved by patterning the SWCNT active layer to confine it to the channel region. For comparative purposes, this improved structure is compared to a traditional structure whose unpatterned SWCNT active layer expands well beyond the channel region. As TFT performance also varies with oxide layer thickness, 90 nm and 300 nm thick oxides were considered. The improved TFTs have gate leakage currents far lower than the traditional TFT with the same dimensions (aside from the unpatterned active area). Moreover, the unusual variation in gate leakage current with applied voltages is resolved. Patterning the SWCNT layer, increasing the oxide thickness, and reducing the top electrode length all help prevent a rapid dielectric breakdown. To take advantage of solution-based fabrication processes, the active layer and electrodes of our TFTs were fabricated with solution-based depositions. The performance of the TFT can be further improved in the future by increasing SWCNT solution incubation time and reducing channel size.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".