A Multiscale Simulation Framework for Steep-Slope Si Nanowire Cold Source FET
Bibliographic record
Abstract
Source engineering is an emerging technique to achieve steep-slope switching FET. To bridge the new carrier filtering mechanism and device performance, a multiscale simulation framework is presented in this article and is applied in Si nanowire (NW) cold source FET (CSFET). By the fit-parameter-free density functional theory (DFT) method, the key component of cold source (CS) design for broken-gap-like band alignment and high cold carrier injection is demonstrated. The novel device switching mechanism is also verified in the entire device scale with fully quantum atomistic tight-binding (TB) and nonequilibrium Green’s function (NEGF) methods. Although these tools are physics-based and accurate, the device scale is limited, and the computation burden is heavy. Thus, half-empirical TCAD simulation is suitable for device design and path-finding in realistic geometry. Key components of the CS and energy filtering effect can be verified by DFT-NEGF and TB-NEGF methods. Based on TCAD results, we implement a circuit-level benchmark for early stage path-finding. The results show that gate-all-around (GAA) Si NW CSFET is a potential candidate for low-power application, which enables supply voltage scaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".