Exploiting Fringing Fields Created by High-<i>κ</i>Gate Insulators to Enhance the Performance of Ultrascaled 2-D-Material-Based Transistors
Bibliographic record
Abstract
High- κ insulators have allowed MOSFETs to obtain extremely high gate capacitances while still suppressing gate leakage. However, using high- κ gate insulators increases the strength of lateral fringing fields throughout MOSFETs, thus causing fringe-induced barrier lowering (FIBL), an electrostatic short-channel effect. The use of high- κ insulators in place of thin low- κ insulators with the same equivalent oxide thicknesses (EOTs) has, therefore, been associated with a tradeoff between a device's electrostatic control and gate leakage. In this study, we use nonequilibrium Green's function (NEGF) simulations to demonstrate that FIBL can be exploited to improve the performance of ultrascaled 2-D-material-based MOSFETs by reducing the impact of source-to-drain tunneling currents that are pervasive in extremely scaled MOSFETs, especially at low currents. As a result, we find that MOSFETs with high- κ gate insulators offer improved performances for low-power applications compared to MOSFETs with low- κ gate insulators at identical EOTs, even before considering the effect of gate leakage.
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) | 0.001 | 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".