Assessing the Role of Dielectric Phase Defects in Doped Ferroelectric HfO<sub>2</sub> Integrated in Negative Capacitance Field-Effect Transistors
Bibliographic record
Abstract
The existence of cubic, tetragonal, and monoclinic phases and the resulting loss in ferroelectricity in doped ferro-electric (FE) HfO2complicate the optimization and practical implementation of FE-HfO2-based negative capacitance (NC) field-effect transistors (FETs). We set out to understand the consequences of these dielectric phase defects in doped FE-HfO2on steep-switching device performance through self-consistent quantum transport simulations. Our findings show that although DE defects worsen the subthreshold swing (SS) of the NC devices, it also favorably narrows the hysteretic window. Defects close to the middle of the potential barrier have the strongest influence on device performance by pushing the top of the barrier down while defects nearing the drain contribute the least to carrier transport and current. Through dimension scaling, we also demonstrate how the contributions from a fixed width of DE defect can be adopted to still generate favorable NC characteristics for logic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".