Analytical Modeling of Line-Tunneling TFETs Based on Low-Bandgap Semiconductors
Bibliographic record
Abstract
The combination of two techniques: low-bandgap semiconductor and line-tunneling structure is an effective way to achieve the highest on-current in TFETs. In this paper, design of low-bandgap line-tunneling TEFT and its analytical modeling of drain current equation is proposed. The previously suggested drain current equation for the low-bandgap line-tunneling TEFT has been explained in a relatively complex form based on the minimum tunnel path that is an effective factor in determining band-to-band tunneling (BTBT). It has been simplified in this paper and reformulated based on gate-to-source voltage. Important design factors such as source doping concentration, material and thickness of the gate-insulator were examined by simulation and numerical calculations based on the minimum tunnel path for two low-bandgap In0.88Ga0.12As and relatively high-bandgap GaSb semiconductors. The comparison of the results obtained from simulations with the proposed analytical drain current model show a good agreement. Drain doping concentration, is an effective factor on the off-state current of low-bandgap TFET. This factor was examined in order to reduce the off-current.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".