A Computational Framework for Gradually Switching Ferroelectric-Based Negative Capacitance Field-Effect Transistors
Bibliographic record
Abstract
While the Landau approach is widely used to model polarization switching of ferroelectric (FE) materials, it cannot accurately describe gradual transition of polarization switching for a stand-alone FE in steady states. To overcome such limitations, the Miller model (MM) was used previously to replicate the switching behavior of FE within an FE–dielectric (FE-DE) capacitor. In this study, we demonstrate a new computational framework for gradually switching FE-based negative capacitance (NC) field-effect transistors (FETs) in steady states. In particular, we solve three modules iteratively: 1) non-equilibrium Green’s function (NEGF) for carrier transport; 2) Poisson’s equation for electrostatics; and 3) the MM for spontaneous polarization (${P}$) versus applied electric field (${E}_{\text{FE}}$). Unlike the FE-DE capacitor, polarization varies along the device position due to the applied field across the device, and hence, polarization interactions are considered. Our simulation result exhibits hysteresis-free, steep-switching characteristics of the NCFET even with the “positive slope” in the${P}-{E}_{\text{FE}}$curve originating from the MM. We have also explicated the physical origin of the experimentally demonstrated critical FE thickness, at which minimum subthreshold swing can be achieved, using two competing mechanisms. Finally, we vary FE parameters (i.e., saturation polarization, remnant polarization, and coercive field) within the MM to investigate their effects on the characteristics of the NCFET. This work not only suggests a novel computational framework for the simulation of the NCFET based particularly on gradually switching FE but also provides irreplaceable physical insight into the optimization of the NCFET by tuning material and device parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".