Electrostatics, scaling, and variability in stacked graphene nanoribbon FETs without metal interlayers
Bibliographic record
Abstract
We present a self-consistent quantum transport simulation of vertically stacked graphene nanoribbon gate-all-around (GAA) field-effect transistors (FETs) without metal interlayers, focusing on the interplay between electrostatics, scaling behavior, and device variability. Removing gate metal interlayers from the conventional GAA structure simplifies the fabrication process but introduces strong electrostatic screening between neighboring graphene ribbons. Our simulations show that increasing inter-ribbon spacing and scaling the sidewall gate oxide substantially improve gate control, enhance the ON/OFF current ratio (ION/IOFF), and suppress short-channel effects, particularly in multi-ribbon configurations. Specifically, when the inter-ribbon spacing exceeds 7 nm, gate efficiency improves significantly, enabling a 3-ribbon device to outperform a monolayer device in ION/IOFF, while only slightly compromising subthreshold swing (SS). Lateral oxide optimization further enhances performance, with ION/IOFF increased by 186% and SS reduced by 27% for the 3-ribbon device when the sidewall oxide is scaled from 4 to 1 nm. Co-optimizing oxide thickness and inter-ribbon spacing also leads to a 36% reduction in SS and a 49% reduction in drain-induced barrier lowering at an 8 nm channel length, compared to an unoptimized 3-ribbon device. Finally, a statistical study with 400 devices of randomly varied geometry demonstrates that these optimizations improve electrostatic robustness and significantly suppress device-to-device variability under realistic misalignment conditions, establishing practical design principles for scalable, low-variability, metal-interlayer-free GAA nanosheet FETs for future electronic applications.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".