The impact of etched trenches geometry and dielectric material on the electrical behaviour of silicon-on-insulator self-switching diodes
Bibliographic record
Abstract
Hole electrical transport in a p-doped nanochannel defined between two L-shape etched trenches made on a silicon-on-insulator substrate is investigated using a TCAD-Medici simulator. We study the impact of the etched trenches' geometry and dielectric filling materials on the current-voltage characteristics of the device. Carrier accumulation on frontiers defined by the trenches causes a modulation of the hole density inside the conduction channel as the bias voltage varies and this gives rise to a diode-like characteristic. For a 1.2 µm-long channel, plots of the electric field distribution show that a nonlinear transport regime is reached at a moderate reverse and forward bias of ± 2 V. Plots of the carrier velocity along the conduction channel show that holes remain hot for a few hundreds of nm outside the nanometre-wide channel, at a bias of ± 10 V. Filling the etched trenches with a high-κ dielectric material gives rise to a lower threshold voltage, V(th). A similar decrease of V(th) is also achieved by reducing the longitudinal and/or the transverse trench width. Our simulation results provide useful design guidelines for future integrated self-switching-diode-based circuits.
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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 | 0.000 | 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".