Embedded ferroelectric nanostructure arrays
Bibliographic record
Abstract
Ferroelectrics hold promise for high-density non-volatile data storage device use. Their eventual performance will strongly depend on the available displacement current, which primarily scales with the area but might to some extent be enhanced by substrate-induced homogeneous strain while the interface at the same time controls the coercive field. As the lateral dimensions persistently decrease, the only way to keep track of the real figures of merit with realistic electrodes is to use a macroscopic configuration instead of scanning probe approaches with undefined interfaces. We report on a novel approach to integrating arbitrarily patterned, highly registered ferroelectric nanoislands fabricated by a template controlled chemical solution deposition approach into a matrix of a low-k dielectric spin-on glass. These structures with a narrow lateral size distribution below 100 nm are subsequently polished in a chemical-mechanical polishing step to expose their very tops, whose piezoelectrically active area depends on the polishing time. At this stage our findings indicate a full piezoelectric functionality of the locally exposed nanoislands. The structures are ready for macroscopic top electrodes to average the displacement current over hundreds of almost identical structures, to provide a nanoscale scaling behaviour of the individual ferroelectric capacitors.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".