Implications of Dielectric Phases in Ferroelectric HfO$_{2}$ Films on the Performance of Negative Capacitance FETs
Bibliographic record
Abstract
Non-homogeneous orthorhombic phase in doped ferroelectric (FE) HfO$_{2}$film presents challenges towards the optimization and performance predictability of negative capacitance (NC) field-effect transistor (FET) performance. We set out to understand the consequences of these dielectric (DE) phases in doped FE-HfO$_{2}$on steep-switching device performance through self-consistent quantum transport simulations. Firstly, we consider a fixed DE phase study to understand how the position, percentage, and number of phase components alter the switching characteristics. Then, to predict device performance variation, we conduct a statistical analysis using a large number of randomly distributed DE phase profiles. We find that DE phases positioned near the center of the potential barrier exert the most significant impact on device performance by lowering the top-of-the-barrier, while those closer to the drain have minimal influence on carrier transport and current. While DE phases in the FE layer degrade the subthreshold swing, they also favorably narrow the hysteretic window, which presents opportunities for optimization in logic devices. Through dimensional scaling and statistical analysis, we demonstrate how optimized performance can be achieved even with large variations in device performance.
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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".