Sustained Benefits of NCFETs Under Extreme Scaling to the End of the IRDS
Bibliographic record
Abstract
We use full quantum-transport simulations by coupling the Landau-Khalatnikov (LK) and Poisson equations self-consistently with the nonequilibrium Green's function (NEGF) formalism, and calibrated to experimental results, to investigate extremely scaled negative-capacitance, field-effect transistors (NCFETs) having dimensions toward the end of the international roadmap for devices and systems (IRDS), that is, to sub-10-nm gate lengths, where channel transport can be expected to be governed by quantum-mechanical effects. We identify how the ferroelectric affects both thermionic emission and quantum-mechanical tunneling of electrons, both of which are relevant transport mechanisms for these ultrascaled devices. Our detailed results show that while NCFETs are not immune to the increase in the tunneling as they undergo extreme channel-length scaling, the metal-ferroelectric-insulator-semiconductor (MFIS) structure will continue to offer benefits to a subthreshold slope, ON- and OFF-currents, drain-induced barrier lowering, and output conductance until the end of the roadmap. These improvements allow MFIS NCFETs of any given node to achieve similar performance to nonferroelectric devices of the immediately preceding (higher-dimension) node. The fundamental reason for the improvements is identified to be the presence of voltage amplification at the top of the barrier (TOB) and suppression of TOB movement with drain voltage.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".