Simulation of Negative Capacitance Based on the Miller Model: Beyond the Limitation of the Landau Model
Bibliographic record
Abstract
Here we demonstrate negative capacitance (NC) characteristics of a ferroelectric–dielectric (FE–DE) capacitor by means of a fully numerical, self-consistent simulation based on the Miller model (MM) and Poisson’s equation. Over the years, the Landau model (LM) has been widely used, which fits experimental data of spontaneous polarization versus electric field (${P}-{E}_{\mathrm {FE}}$) characteristics using a so-called “S-curve”; however, it cannot capture different transitions of polarization switching and can also fail to properly represent the material properties of certain FEs. To overcome such limitations of the LM, we have used the MM to simulate an FE–DE capacitor. Even though the MM seemingly fails to show steep switching characteristics due to the absence of the negative slope in the${P}-{E}_{\mathrm {FE}}$curve unlike the LM, our simulation exhibits the NC characteristics of FE–DE capacitors with significant internal voltage amplification. Notably, we explore the effect of different transitions of polarization switching by varying the coercive field of FE within the MM, and exhibit that greater NC characteristics can be achieved with a FE having a more abrupt switching transition. We have also investigated the impact of other material parameters of FE, such as saturation and remnant polarization, on the NC characteristics of FE–DE capacitors. Our results provide comprehensive insight into the mechanism of FE-DE capacitors, suggesting sophisticated engineering of material and device parameters to seek desired performance of NC devices.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".