Investigating the Performance of Sub-10-nm WSi<sub>2</sub>N<sub>4</sub> MOSFETs With Native Dielectric Through a Machine Learning Tight-Binding Framework
Bibliographic record
Abstract
Monolayer WSi2N4has emerged as a promising 2-D semiconductor for high-performance ultrascaled MOSFETs. In this work, we use machine learning techniques to generate a sparse tight-binding (TB) Hamiltonian and evaluate the performance of sub-10-nm n-type and p-type WSi2N4MOSFETs with native Si3N4as the gate dielectric. To validate our approach, we compare theI–Vcharacteristics generated by our machine learning TB (MLTB) model with those obtained from a TB model using the maximally localized Wannier function (MLWF) approach for a monolayer HfS2MOSFET, demonstrating excellent agreement. Our results show that both n-type and p-type WSi2N4MOSFETs meet the International Roadmap for Devices and Systems (IRDS) 2022 ON-current (Ion) target for high-performance (HP) applications at channel lengths (Lch) of 5–10 nm. For high-density (HD) applications, n-type and p-type devices can be scaled down to 7 and 8 nm, respectively, while maintaining IRDS compliance. At a 10-nm channel length, n-type devices achieve a higherIonthan p-type devices, while both exhibit comparable subthreshold swing (SS) close to the 60-mV/dec limit at room temperature. However, asLchdecreases, n-type devices experience greater SS degradation than p-type devices due to enhanced source-to-drain tunneling, allowing p-type devices to outperform at shorter channel lengths. In addition, transport simulations reveal directionally isotropic carrier behaviors in WSi2N4. These findings underline the potential of WSi2N4for next-generation ultrascaled transistors and showcase the utility of machine-learning-based approaches in modeling devices constructed with novel 2-D materials.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".