A Self-Consistent Full 3-D Real-Space NEGF Simulator for Studying Nonperturbative Effects in Nano-MOSFETs
Bibliographic record
Abstract
In this paper, we present a full 3-D real-space quantum-transport simulator based on the Green's function formalism developed to study nonperturbative effects in ballistic nanotransistors. The nonequilibrium Green function (NEGF) equations in the effective mass approximation are discretized using the control-volume approach and solved self-consistently with the Poisson equation in order to obtain the electron and current densities. An efficient recursive algorithm is used in order to avoid the computation of the full Green function matrix. This algorithm, and the parallelization scheme used for the energy cycle, allow us to compute very efficiently the current-voltage characteristic without the simplifying assumptions often used in other quantum-transport simulations. We have applied our simulator to study the effect of surface roughness and stray charge on the ID-VGcharacteristic of a 6-nm Si-nanowire transistor. The results highlight the distinctly 3-D character of the electron transport, which cannot be accurately captured by using 1-D and 2-D NEGF simulations, or 3-D mode-space approximations.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".