Performance Limit Projection of Germanane Field-Effect Transistors
Bibliographic record
Abstract
Here we explore the performance limit of monolayer germanane (GeH) field-effect transistors (FETs). We first plotted an electronic band structure of GeH using density functional theory and then tight-binding parameters were extracted. Device characteristics of GeH FETs are investigated using rigorous self-consistent atomistic quantum transport simulations within tight-binding approximations. Our simulation results indicate that GeH FETs can exhibit exceptional on-state device characteristics, such as high Ion (>2 mA/μm) and large gm (~7 mS/μm) with VDD= 0.5 V due to the very light effective mass of GeH (0.07m0), while maintaining excellent switching characteristics (SS~64 mV/dec). We have also performed a scaling study by varying the channel length, and it turned out that GeH FET can be scaled down to ~14-nm channel without facing significant short channel effects but it may suffer from large leakage current at the channel length shorter than 10 nm. Finally, we have benchmarked GeH FET against MoS2counterpart, exhibiting better suitability of GeH device for high-performance applications compared with MoS2transistors.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".