Short channel vertical transistors with excellent saturation characteristics
Bibliographic record
Abstract
The use of a vertical thin film transistor (VTFT) topology in the flat panel active matrix array, opens up a plethora of new high performance applications. The VTFT is small in footprint by virtue of its stacked layer configuration, in which channel lengths can be conveniently scaled down to nanometer regime without having to resort to photolithography as would otherwise be needed when scaling lateral TFTs. More importantly, the VTFT is also fully compatible to the materials and processes used in flat panel technology, takes making it amenable to large area scaling. In the VTFT, the source and drain electrodes are vertically stacked and separated by an intermediate insulator layer. The channel is formed on the vertical sidewall of the source/insulator/drain stack. Since the thickness of intermediate insulator layer defines the channel length, this can now be accurately controlled at the nanometer scale via the thickness of the insulator layer. While nanoscale channel length VTFTs in amorphous silicon (a-Si) have been demonstrated previously, one of the biggest issues was the lack of good saturation behavior at high drain voltages, which made reduction of the gate dielectric thickness mandatory. However, reducing the gate dielectric thickness leads to high gate leakage and early dielectric breakdown. This presentation is on short-channel VTFTs with excellent saturation characteristics, achieved by an ultra-thin silicon nitride (SiNx) gate dielectric using plasma-enhanced chemical vapor deposition (PECVD).
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 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) | 0.003 | 0.001 |
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.
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".