Dual source negative capacitance GaSb/InGaAsSb/InAs heterostructure based vertical TFET with steep subthreshold swing and high on-off current ratio
Bibliographic record
Abstract
Continuous downscaling of CMOS technology at the nanometer scale with conventional MOSFETs leads to short channel effects (SCE), increased subthreshold slope (SS), and leakage current, degrading the performance of ICs. We proposed a dual-source vertical tunnel field-effect transistor (TFET) with a steeper subthreshold swing (SS) and superior electrostatic control thanks to quantum mechanical band-to-band tunneling. We show that the use of GaSb/InGaAsSb/InAs heterostructure boosts the band-to-band tunneling rate in TFETs, resulting in higher on-state current. Incorporating the negative capacitance effect using ferroelectric materials further enhances the performance of the proposed device greatly. The lowest SS of 21.94 mV/dec and an on–off current ratio of 4.3267 × 1011 were obtained for dual source GaSb/InGaAsSb/InAs heterostructure based vertical TFET. The lowest subthreshold swing was found as 17.37 mV/dec after integrating Hf1–xZrxO2 ferroelectric material into the gate stack. The negative capacitance effect also increases the on-state current tenfold, resulting in an incredible ION/IOFF ratio of 1012. The suggested device focuses on low power consumption applications by assuring a very low leakage current and a reduced subthreshold swing.
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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".